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TitleArtificial immunity inspired cooperative failure recoveryframework for mobile multi-robot system

Advisor(s) Lau, HYK

Author(s) Chan, Ching-man; –sk ‡

Citation

Issued Date 2014

URL http://hdl.handle.net/10722/206677

RightsThe author retains all proprietary rights, (such as patent rights)and the right to use in future works.


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Artificial Immunity Inspired Cooperative

Failure Recovery Framework for Mobile

Multi-Robot System

By

C. M. Chan

B. Sc. H.K.

A thesis submitted in partial fulfilment of the requirements for

the degree of Doctor of Philosophy

at The University of Hong Kong

June 2014


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I

Abstract of thesis entitled

Artificial Immunity Inspired Cooperative Failure Recovery

Framework for Mobile Multi-Robot System

Submitted by

C. M. Chan

for the degree of Doctor of Philosophy

at The University of Hong Kong

in June 2014

Robots are sophisticated machines which are specially designed to have the

capabilities to handle operations, on behalf of human, in many different scenarios. In

the past decades, the design of robot systems has been evolving and there are

increasing numbers of possible applications of robot. Some systems can even be able

to overcome the individual limitations and handle complex problems by combining

the strengths of multiple robots.

To reduce the risk of human life, robots are now being put into missions under

extremely dangerous or hazardous environment where human intervention is not

tolerable , such as search-and-rescue missions inside damaged buildings after natural

disasters and cleaning up of radioactive materials in nuclear accidence. Even though

robots are dispensable, if they are damaged, disabled or trapped, the mission would

not be accomplished. Therefore, the longevity of a robot system is always a challenge

for robotic operations in such difficult environments.

To tackle this challenge, many studies focus on improving the design of individual

robot, minimizing the chance of robot failure; or the way that how functioning robots

may share the job of the failed robots. The way that how other robots can help failed

robots recover, however, has yet to be widely discussed.


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II

This thesis studies the feasibility of having multi-robot system with different

automatic cooperative recovery abilities on top of its primary functions. A novel

cooperative recovery framework is proposed for generic control among system

primary functions and recovery behaviours. A number of experiments have been done

to study the influence of cooperative recovery on a multi-robot system and how it can

affect the system in terms of system performance, sustainability and overhead. An

Immunity-based cooperative recovery model has also been created to overcome the

drawback introduced by cooperative recovery, finding a balance between the two

system objective among system productivity and longevity.

Two modified versions of cooperative recovery model are also included in this study

to further maximize the system potential.

.

.(322 words)


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I

Declaration

I declare that the thesis represents my own work, except where due

acknowledgement is made, and that it has not been previously included in a thesis,dissertation or report submitted to this University or any other institution for a degree,

diploma or other qualifications.

__________________________

C. M. Chan

June 2014


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II

Acknowledgements

I would like to express my most sincere gratitude and deepest appreciation to my

supervisor, Dr. Henry Y. K. Lau, for all his encouragement, assistance andenthusiastic guidance throughout the course of my PhD study. Especially when I

encounter the very difficult moments in my life, leaving of family members, he gave

me invaluable chances, encouragements and inspirations which are not only essential

to complete this thesis, but also courage on future personal development.


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III

Table of Contents

Declaration ..................................................................................................................... I

Acknowledgements ....................................................................................................... II

Table of Contents ......................................................................................................... III

List of Figures .............................................................................................................. VI

List of Tables ............................................................................................................ VIII

List of Symbols ............................................................................................................ IX

Chapter 1 Introduction .............................................................................................. 1-1

1.1. Goal of Thesis ............................................................................................. 1-3

1.2. Contributions ............................................................................................... 1-3

1.3. The Outline .................................................................................................. 1-4

Chapter 2 Literature Review ...................................................................................... 2-1

2.1. Automatic Recovery on Robot Systems...................................................... 2-2

2.1.1. Single Robot System and Recovery ........................................................ 2-2

2.1.1.1. Assembly Robots ................................................................................. 2-2

2.1.1.2. Mobile Robots ...................................................................................... 2-4

2.1.1.2.1. Robot Recharging Mechanics .......................................................... 2-6

2.1.2. Multi-Robot System and Recovery ......................................................... 2-8

2.1.2.1. Task and Resources Re-allocation ....................................................... 2-8

2.1.2.2. Replaceable Modules ........................................................................... 2-9

2.1.2.3. Mobile Recharging Mechanics .......................................................... 2-11

2.1.2.4. Inter-robot Docking ........................................................................... 2-12

2.1.2.5. Communications Recovery ................................................................ 2-14

2.2. Artificial Immunity ................................................................................... 2-15

2.2.1. Human Immune System ........................................................................ 2-15


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IV

2.2.2. Artificial Immune System ..................................................................... 2-18

2.2.2.1. Immune Inspired Algorithms ............................................................. 2-18

2.2.2.1.1. Negative Selection theory .............................................................. 2-18

2.2.2.1.2. Clonal selection theory ................................................................... 2-19

2.2.2.1.3. Immune network theory ................................................................. 2-21

2.2.2.2. Immune-Based Single Robot Control ................................................ 2-24

2.2.2.3. Immune-Based Multi-Agent System Control .................................... 2-25

2.3. Summary ................................................................................................... 2-30

Chapter 3 Cooperative Recovery Model.................................................................... 3-1

3.1. Cooperative Failure Recovery ..................................................................... 3-1

3.2. Recoverable Mechanics............................................................................... 3-2

3.2.1. Parts Replacing ........................................................................................ 3-3

3.2.2. Strength Combining ................................................................................. 3-3

3.3. Different roles of robots .............................................................................. 3-4

3.4. The Cooperative Recovery Trade-off Problem ........................................... 3-6

3.5. Immunity Inspired Recovery Model ........................................................... 3-8

3.5.1. A Two-level Response Architecture........................................................ 3-9

3.5.2. The Control System ............................................................................... 3-12

3.5.3. Design of Antibodies ............................................................................. 3-14

3.5.4. Classification of Recovery Operations .................................................. 3-16

3.5.5. Behavioural Decision Making Algorithm ............................................. 3-17

3.5.6. Favourability Functions of Operations .................................................. 3-21

3.5.6.1. Productive Function ........................................................................... 3-21

3.5.6.2. Recovery Function ............................................................................. 3-22

3.5.6.3. Cost Function ..................................................................................... 3-23

3.6. Experiments and Analysis ......................................................................... 3-24

3.6.1. Influence of Cooperative Recovery on Multi-Robot System ................ 3-24


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3.6.2. On-site Cooperative Recovery Mechanics ............................................ 3-29

3.7. Summary ................................................................................................... 3-31

Chapter 4 Sacrificing Cooperative Recovery Model ................................................ 4-1

4.1. Concept of Sacrifice .................................................................................... 4-2

4.2. Resources Sharing Recoverable Mechanics ................................................ 4-3

4.3. Role of the Grey Robot ............................................................................... 4-4

4.4. Cost Function for Sacrificing Recovery ...................................................... 4-5

4.5. Experiments and Analysis ........................................................................... 4-6

4.6. Summary ..................................................................................................... 4-7

Chapter 5 Productivity Boost Recovery Model ......................................................... 5-1

5.1. Sustainability Confidence and Productivity ................................................ 5-2

5.2. Experiments and Analysis ........................................................................... 5-3

5.3. Summary ..................................................................................................... 5-6

Chapter 6 Conclusions ............................................................................................... 6-1

5.4. Research Summary ...................................................................................... 6-1

5.5. Future Works ............................................................................................... 6-4

References ................................................................................................................... R-i


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VI

List of Figures

Figure 1 Photography of the snake robot which recover from damage on muscle wires

(Mahdavi and Bentley 2003) ................................................................... 2-4 Figure 2 Quadrupedal robot (top) and hexapedal robot (bottom) used in (Bongard and

Lipson 2004a, 2004b), ............................................................................. 2-5

Figure 3 Photography of the recharging station (top) and corresponding docking

mechanism (bottom) in Silverman et al. (2002, 2003) ............................ 2-6

Figure 4 Robot which autonomously plugging itself into electricity slot (Meeussen et

al. 2010) ................................................................................................... 2-7

Figure 5 Self-repair process of modular reconfigurable robots (Murata et al. 2001) ....

…………………………………………………………………………...2-9

Figure 6 Photography of FRACTUM robot doing Self-repairing (Yoshida et al. 2003)

.................................................................................................................. 2-9

Figure 7 Vision of self-sufficient robot colonies (Bererton and Khosla 2001) ...... 2-10

Figure 8 The “baby” robots (top), “mother” robot with recharge and docking station

mounted on it body (bottom) (Drenner et al. 2013) .............................. 2-11

Figure 9 JL-2 docking manipulator and grasping modules (Wang, et al. 2010) ..... 2-12

Figure 10 JL-2 lifting up the middle robot (Wang, et al. 2010)............................... 2-13

Figure 11 Robot team with robot 1 serving as a relay node and maintaining line of

sight with robot 0 and 2 during a mission where global communication in

not available (Sweeney et al. 2002) ...................................................... 2-14

Figure 12 Different Immune Cells in Human Immune System ............................... 2-16

Figure 13 Concept of idiotypic networks................................................................. 2-21

Figure 14 Immunoid robot in experiment using immune network based decision

making (Ishiguro, Watanabe et al. 1996). ............................................ 2-24

Figure 15 Functional Modules Architecture by Dasgupta (1998) ........................... 2-26

Figure 16 The General Suppression Control Framework by Ko et al. (2004) ......... 2-27

Figure 17 Architecture of control framework for individual AIS agents by Lau (2006)

................................................................................................................ 2-28

Figure 18 The change in robot state and role ............................................................. 3-5

Figure 19 The performance of system drops when recovery operations in place ..... 3-7


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VII

Figure 20 The two-level defence mechanism in human immune system and proposed

model...................................................................................................... 3-10

Figure 21 Flow of operation from perception to behaviours ................................... 3-13

Figure 22 Design of N-Cell and S-Cell.................................................................... 3-14

Figure 23 A real Pioneer I robot (left) and a virtualized Pioneer robot in Player/stage

................................................................................................................ 3-24

Figure 24 Ten robots working in the simulation environment with stochastic damage

rate on its wheel inflicted by hazardous surface, in 2D and 3D View ... 3-25

Figure 25 System Life and productivity of different setups in experiment A ......... 3-27

Figure 26 Experiment setup A2 at 600 decision cycles (top) A3 at 500 decision cycles

(bottom).................................................................................................. 3-28

Figure 27 Result of Experiment B with additional recovery mechanism ................ 3-30

Figure 28 The robots transforming into Grey robot................................................... 4-4

Figure 29 Result of Experiment C with the sacrificing recovery mechanism

comparing with Setups A5 and B ............................................................ 4-6

Figure 30 Result of the Productivity Boost Recovery Model with setup A5, B, C1 &

C2 ............................................................................................................. 5-3

Figure 31 The average maximum continuous productive time in A5, B, C1 & C2 .. 5-4

Figure 32 Area coverage of Setup A (i) without Boost and (ii) with Boost; Setup B (iii)

without Boost and (iv) with Boost. .......................................................... 5-5


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VIII

List of Tables

Table 1 Recovery Strategies used in (Baydar and Saitou 2004) ............................... 2-3

Table 2 Communication Recovery Behaviour in (Ulam and Arkin 2004) .............. 2-14

Table 3. Colour of role in particular roles for illustration purpose ............................ 3-4

Table 4. Relationships between the proposed recovery model and the immune system

.................................................................................................................................. 3-11

Table 5. Difference among setups in experiment A ................................................ 3-26

Table 6. Refined role models for Sacrificing Cooperative Recovery ........................ 4-4


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IX

List of Symbols

actions of robots

normal actions

sustainment actions

failure recovery actions

mandatory maintenance operations

sustainment response

normal responses

self-sustainment response

cooperative sustainment response

p population size

sustainability ratio

n number of N-cell being selected

s number of S-cell being selected

sustainability level

estimated health of the robot α maximum possible health of the robot α

number of working robots within distance d from robot

number of failed robots within distance d from robot

favourability of an antibody x


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X

expected productive value of an antibody x

number of favourable memory of an antibody x

number of unfavourable memory of an antibody x

influence from other triggered antibodies on an antibody x

stimulation from antibody y to antibody x

suppression of antibody z to antibody x

expected gain in production value after the response

expected loss of production during the recovery

favourable threshold of the robot α


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1-1

Chapter 1

Introduction

One major motivation to develop autonomous robotic solutions is to reduce the need

for human presence in hazardous environments and to relieve humans from

performing highly repetitive manoeuvres (Matarić, 2007). Especially after several

serious disasters, such as the World Trade Centre Collapse and Tohoku Fukushima

nuclear event, even more attention has been focused on the robot system which can be

deployed and work in extremely dangerous and unpredictable situations (Murphy2012, NHK 2013).

Most traditional approaches to build autonomous robotic solutions involve creating a

single “mighty” robot with capabilities necessary to accomplish the entire mission on

its own. For small-scale applications, this is by far the most efficient means of system

design; yet as task complexity increases, such as real world applications that are

inherently distributed in time, space and functionality, single robot design has

significant limitations as well as a single point of failure if the robot is disabled for

any reasons. A multi-agent concept has therefore emerged as the solution to this

problem (Parker 2003).

In a multi-agent based robot system, also known as a multi-robot system, a large-scale

mission is decomposed and sub-missions are allocated to a team of robots. These

robots inter-coordinate to support and complement each other, performing tasks and

interchanging duties to achieve a common goal for system-wide mission

accomplishment. Many studies show that this modelling concept of multi-agent

systems (MAS) is very useful and efficient for complex robotic operations such as

firefighting (Cohen et al. 1990, Liao 2005, Yan-Tao et al. 2009), hazardous waste

clean-up (Kurabayashi et al. 1996, Parker 1998, Jager and Nebel 2002), search and

rescue (Jennings et al. 1997, Nourbakhsh, Sycara et al. 2005, Baxter, Burke et al.

2007), planetary exploration (Mataric and Sukhatme 2001, Huntsberger et al. 2003)

and military reconnaissance (Rybski et al. 2000, Wagner and Arkin 2004, Kolling and

Carpin 2008). However, in spite of all the advantages MAS has to offer, the


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effectiveness of the multi-robot system is still hard to control if its robots cannot

function. The presence of unanticipated circumstances and dangers in the many real

world environments can always deviates the system from its expected behaviour,

disabling the robots or even rendering the system to a complete failure. This poses a

very important problem of effective and efficient failure management among the

multi-robot system designs to cope with real world applications.

Many studies have been done on the recovery of robot system in past decades. Some

works focused on fault tolerance and task reallocation after robot failure occurs (Toye

and Leifer 1994, Visinsky et al. 1994, Parker 1998); some studies even suggested

integrations of automatic recovery (Gini and Gini 1984, Portugal and Rocha 2013),

including robots with ability to repair and reconfigure itself (Mahdavi and Bentley

2003, Bongard and Lipson 2004). However, the area of automatic cooperative failure

recovery, such as having one of the robots actually helps in the action of recovering

other failed robots, is yet to be fully explored.

There are two main problems in developing a cooperative failure recovery-enabled

robot system. First, the robotic mechanism, which allows the actual failure recovery

interaction among robots, would greatly increase the complexity of the robot design.

This mean the cost invested on developing the robot would be increased. Second,

even if the mechanism is in place, it is hard to coordinate the robot and make the

system cost-effective. The overall system productivity can be further reduced due to

the introduction of those recovery mechanisms, as the system production rate which

already suffering from robot failure would be further slowed down when having a

functioning to participant in the recovery of a failed robot. A good system control

strategy is required in order to make the automatic cooperative recovery failure

favourable.

This thesis focuses on the highly tolerant and adaptive cooperative failure recovery

models, which are inspired by the biological immune system. The theoretical

underpinning of the scheme is based on the formalism of the immune network theory

inspired from artificial immune systems (AIS), which is an engineering analogy of the

human immune system and a powerful artificial intelligence (AI) methodology.

Previous studies (Dasgupta 1998, Lau and Wong 2006, Lu and Lau 2009) have shown

that AIS offer the favourable features of self-regulation and self-organization,


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1-3

adaptation and coordination for providing homeostatic control to a robotic system and

producing the best solution to a problem in a distributed manner.

The proposed methodology helps to maintain multi-robot system sustainability and

dependability in a cost-effective manner; also examine how the incorporating of

cooperative recovery may influence the future of multi-robot system, unleashing

overall system potential in stochastic environments.

1.1. Goal of Thesis

The objective of this research is to provide a decent motivation and advocate the

development of diverse cooperative recovery strategies in future multi-robot system

through the establishment of the universal cooperative failure recovery models.

1.2. Contributions

The contributions of this thesis are:

The general and unique recovery model for cooperative recovery of

multi-robot system

A control framework for balancing the benefits and overhead which

introduced recovery strategies

The construction of scarifying model for integration sophisticated

cooperative recovery methodology

The demonstration of possible performance boost in respectable

confidence levels


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1-4

1.3.

The Outline

The thesis proceeds as follows:

Chapter 2 provides background for this study by looking into the potential robot

recovery mechanisms, artificial immune system and the previous work done on multi-

robot system using immunity inspired intelligence.

Chapter 3 describes the proposed Basic Cooperative Recovery Model (BCRM) and

how cooperative recovery is achievable, as well as evaluating its possible potential.

Chapter 4 proposes the modified version of the cooperative recovery model which

incorporated with the concept of sacrifice. This Scarifying Cooperative Recovery

Model (SCRM) allows good of sacrifice

Chapter 5 introduce a modified version of the cooperative recovery model which

specially designed to maximize the amount time a robot can stay on the field before

each returning.

Chapter 6 summarizes and concludes the findings of this research and future works.


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2-1

Chapter 2

Literature Review

This chapter reviews the projects and studies which influence the model proposed in

this thesis. This section can be separated into two sections: the first one is to reviews

the development of robot recovery and related mechanisms which can be applied to

the proposed new recovery model; the second one is to review several related

biological immunity inspired artificial intelligence which provide inspirations to this

study.

As the focus of this study is failure recovery, the development failure detection would

not be reviewed here. Section 2.1would begin with the automatic recovery mechanism

in single robots, and then followed with additional automatic recovery mechanism

only multi-robot system can perform.

Artificial immunity is the key inspiration of this study, Section 2.2 would show some

background of immunity algorithms and related studies of robot systems which

applying the artificial immunity.


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2-2

2.1.

Automatic Recovery on Robot Systems

Failure recovery of robot system is complicated and system dependent. A single

recovery method may involve multiple problem domains. Not every system failure

has an existing recovery mechanism, yet the recovery mechanisms are slowly building

up.

The followings are collection of research studies with possible mechanism and related

works which maybe applicable to implement cooperative recovery.

2.1.1.

Single Robot System and Recovery

Srinivas (1977) may have been the first study on error recovery of robot

system, he proposed a method to diagnose the system situation using a pre-

defined decision tree. After that, research studies on automatic recovery start

getting more attention since 80’s and 90’s.

2.1.1.1. Assembly Robots

The earliest research studies on automatic robot recovery mainly focus on

error detection and error diagnosis of internal system failure in single robot

systems for manufacturing and assembly production line. Their motivations

are to reduce the production defections and maximize the profit. The failure

recovery operations can be done on programming level (Gini and Gini 1984,

Kao 1995) along with real time monitoring (Smith and Gini 1986, Kim and

Welch 1989).


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2-3

Later on, attention starts drawing on the automatic generation of recovery

knowledge with machine learning, such as the framework being introduced to

generating automatic recovery plan by Evans and Lee (1994). The framework

offers quickly correction of known errors and automatically learns to recover

new errors without human intervention. However, that was designed for stasis

environment and not much detail about actual recovery strategies has been

discussed there.

Bavdar & Saitou (2004) proposed the first off-line virtual diagnosis and

recovery method using Bayesian reasoning and genetic programming which

helps to reduce the downtime of assembly robot systems.

Table 1 shows the five recovery strategies being applied in its

experiments. Although the strategies are simple, it still gives examples of

possible recovery strategies for assembly robot.

Table 1 Recovery Strategies used in (Baydar and Saitou 2004)

Problem Strategy

Grasping error Try grasp again or release again

Collision error Collision recovery

Sensor failure Call maintenance

Misplacement error Pickup and replace the part

Flawed parts Dispose the part


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2.1.1.2.

Mobile Robots

There are more recovery mechanics usable for robot recovery. Figure 1 shows

a snake robot designed by Mahdavi & Bentley (2003, 2006) which can recover

from damage on its muscle with the evolutionary algorithm and shape memoryalloy muscle. This robot can continue locomotion by evolving new movement

strategy even some of its muscle wires are damaged. By using this approach, it

is feasible to develop robots arms and robot legs which can self-recover from

damages.

Figure 1 Photography of the snake robot which recover from damage on muscle wires

(Mahdavi and Bentley 2003)

In the study of Bongard & Lipson (2004a, 2004b), there even suggested an

estimation-exploration algorithm which allows a quadrupedal robot and

hexapedal robot to continue walking with presence of broken legs, jamming

joints and failed angle sensors. Figure 2 shows the photography of simulated

robots used by Bongard & Lipson, where M are the positions for motorized

joints, A are angle sensors and T are touch sensors.

There are also electronic circuit for robots which can be recovering from

damage according to the study from Keymeulen et al. (2000).


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2-5

Figure 2 Quadrupedal robot (top) and hexapedal robot (bottom) used in (Bongard and

Lipson 2004a, 2004b),


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2.1.1.2.1.

Robot Recharging Mechanics

Power failure is another issue which always bring troubles to autonomous

mobile robot. Besides having backup batteries, Silverman et al. have

developed a recharging station and corresponding docking mechanism in(Silverman et al. 2002, 2003) which allow Pioneer 2DX robots to recharge

autonomously. Figure 3 shows a photography of a Pioneer 2DX robot going to

be docked at the charging station. Another similar vision-based self-docking

and recharging can be found in studies of Kartoun et al. (2006).

Figure 3 Photography of the recharging station (top)

and corresponding docking mechanism (bottom) in Silverman et al. (2002, 2003)


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2-7

By integration robotic arm and vision sensors, Meeussen et al. (2010) even can

make a robot to open doors in office environment and plug itself to into the

electricity socket for power recharging. Figure 4 shows their experimental

robot opening office door and searching for electricity slot to recharge itself.

Figure 4 Robot which autonomously plugging itself into electricity slot (Meeussen et al. 2010)


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2.1.2.

Multi-Robot System and Recovery

Since the advent of distributed robotics in the late 1980s, extensive research

has been carried out on different aspects of multi-robot systems (Parker 2003).

The majority of studies include task planning (Alami et al. 1998), inter-robot

communication and decision making (Molnár and Starke 2000), formation

control (Gu and Hu 2009), localization (Roumeliotis and Bekey 2002),

exploration (Howard et al. 2006), motion coordination (Yamaguchi 2003),

task allocation (Strens and Windelinckx 2005) and learning (Fernandez et al.

2005). Although not many of these studies directly address the problem of

failure recovery, there are some related studies carried out under the broad

topic of fault tolerance.

2.1.2.1. Task and Resources Re-allocation

Parker (1998) proposed a widely adopted software architecture, known as

ALLIANCE, for fault tolerance control among heterogeneous multi-robot

systems, with its capabilities evaluated in later works (Parker 2000, 2001).

Gerkey and Mataric (2002) then discussed the problems of ALLIANCE and

introduced certain provisions for robot failure with “Pusher -Watcher”

experiments. Dias et al. (2004) proposed a multi-robot coordination approach

to minimize the performance degradation for three categories of robot failure:

communication failure, partial robot malfunctions and robot death. These

research studies focus mainly on the reallocation of tasks and resources after

robot failure occurred. Although they proposed different way on failure

handling, they are not directly related to robot failure recovery strategies.


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2.1.2.2.

Replaceable Modules

A more direct approach for robot failure recovery is building the robot system

a self-repairing mechanical system (Murata et al. 2001), which can repair itself

without any human intervention. As illustrated in Figure 5, this involving

spare units of the robot being transformed and replacing the damaged units.

This approach can be realised using modular reconfigurable robots such as

CONRO (Castano et al. 2000), Polybot (Yim et al. 2000), and lattice type

reconfigurable robots (McGray and Rus 1998).

Figure 5 Self-repair process of modular reconfigurable robots (Murata et al. 2001)

Figure 6 showing a group of FRACTUM reconfigurable robots by Yoshida et

al. (2003) doing self-repairing, cutting off the failed units and use spare units

to replace and fill up the original position of the failed one.

Figure 6 Photography of FRACTUM robot doing Self-repairing (Yoshida et al. 2003)


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Apart from self-repairing, another direct approach to robot failure recovery

would be cooperative repairing. Bererton and Khosla (2001, 2002) visioning

the building of a self-sufficient robot colonies on distant plants using robots

with repair capabilities showing in Figure 7. They proposed a repairable robot

team, which promote module replacement when failures occur, such as

scavenging usable modules from failed robots and use them to repair future

failures.

Although human operators were still required in their experiment, their studies

placed one step toward cooperative repairing.

Figure 7 Vision of self-sufficient robot colonies (Bererton and Khosla 2001)


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2.1.2.3.

Mobile Recharging Mechanics

For the recharging problem, instead of having a single robot returning to a

charging station and recharge itself, multi-robot system can take other

approach to tackle the problem of recharging. Zebrowski and Vaughan (2005)

and Couture-Beil and Vaughan (2009) suggested using a specialised “tanker”

robot in the field to help recharge other robots in the team. This approach

allow the robot to stay more time on working, reducing the time spent on

travelling back and forth from base.

Another approach is to build the multi-robot system as a marsupial robot team

(Murphy et al. 1999), where smaller “baby” robots are deployed from a big

“mother” robot. Drawing on the marsupial concept, Drenner et al. (2013) even

designed a mobile recharging marsupial robot team for exploring mission.

Figure 8 showing the photography of the marsupial robots with docking

mechanics which can recharge multiple robots at the same time. When the

mother robot moving around in the workplace, the baby robots can dock and

recharge from it.

Figure 8 The “baby” robots (top), “mother” robot with recharge and docking station

mounted on it body (bottom) (Drenner et al. 2013)


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2.1.2.4. Inter-robot Docking

The ability for robot to grasp and dock with other robots can also be useful in

many situations. While developing new reconfigurable robot, several studies

has introduced docking and manipulating capabilities to mobile multi-robot

system (Zong et al. 2006, Li et al. 2008, Wang et al. 2010). They designed a

docking manipulator and grasping modes in JL-2, which allow mobile robots

to grasp and dock with each other. Figure 9 shows the design of gripper and

dock in JL-2.

Figure 9 JL-2 docking manipulator and grasping modules (Wang, et al. 2010)


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In Figure 10 a robot in the middle is being lifted up by robots from two sides,

where G, NG, θ and M represents the weight of the robot, supporting force

from ground, angle and torques of motors respectively. This mechanism can

be useful in retrieve damaged robots or rescuing robots which being flipped

over or trapped on the ground.

Figure 10 JL-2 lifting up the middle robot (Wang, et al. 2010)


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2.1.2.5.

Communications Recovery

In multi-robot systems, if robots are not able to communicate with each other,

it would be hard for them to coordinate their operations. Therefore, there are

research stuides focusing on the alternative means of communication, such as

maintaining line of sight networks in (Arkin and Diaz 2002, Sweeney et al.

2002, Anderson et al. 2003, Nguyen et al. 2003). This kind of studies tries to

provide the robot team an alternative communication channel in case the

global primary communication channel cannot be used.

Figure 11 Robot team with robot 1 serving as a relay node and maintaining line of sight

with robot 0 and 2 during a mission where global communication in not available

(Sweeney et al. 2002)

However, if there is not backup communication method can be used, Ulam &

Arkin (2004) proposed a set of communication recovery behaviours, such as

moving from ground surface to higher location and moving to nearest

neighbour, for individual robots in the system to reactive when

communication goes wrong. Table 2 shows the four basic recovery elements

used to form combination of recovery behaviour.

Table 2 Communication Recovery Behaviour in (Ulam and Arkin 2004)

Communication Recovery Behaviour Rationale

Retro-traverse Moving back to previous location for regaining

communication

Move-To-Nearest-Neighbor Maintaining line of sight

Move-To-Higher-Ground Higher surface trends to have better reception

Probe Moving away from area with reception blocked


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2.2.

Artificial Immunity

Human immune system (HIS) is a complex, self-protected and self-maintained

cooperative defense system in human body (Dasgupta 1999, Ishida 2004) that ensures

the survival of mankind for thousands years by protecting human body from the

invasion of foreign substances such as bacteria and virus. Behind this well-composed

system inside our body, there are some sophisticated cooperative mechanisms that

cooperate the trillions of immune cells to response to unpredictable invasion and

attack of pathogens rapidly. By extracting the concepts behind HIS, we can adopt

them as metaphors and engineering paradigms, to solve different problems in real life,

such as negative selection (Forrest et al. 1994),, clonal selection theory (Burnet 1959)

and immune network theory (Farmer et al. 1986).

2.2.1. Human Immune System

HIS protects our body against pathogens by distinguishing, eliminating and

memorizing foreign cell and macromolecules, it involves two-layer line of defense,

the innate defense and the adaptive defense (Sompayrac 1999).

Innate immune defense serves as the first barrier which tries to stop pathogens

entering our body and minimizes the chance of being infected; while the adaptive

immune defense activates if pathogens evade from innate response and produce

antibodies to fight and recover from infection (Purves et al. 2001). There many type

immune cells in human body. Figure 12 shows key categories of cells in the two

defense layer.


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Figure 12 Different Immune Cells in Human Immune System

Innate

System

Adaptive

System

Phagocytes

Macrophages

Monocytes

Dendritic Cells

Neutrophils

Eosinophil

Basophils

Lymphocyte

T-Cell

B-Cell

Memory T

Killer T

Helper T

Memory B

Plasma Cell


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Innate defense provides a general, non-specific resistance to a variety of invasions,

throughout their first exposure to human body by eliminating macromolecules which

identified as foreign pathogen (non-self). The macrophages are cells which eliminate

and eat pathogen.

Adaptive defense provides specific response which recognizes any pathogens which

cannot be removed by the innate defense. The plasma cell would create antibodies

which target specific pathogens. On the surface of antibodies, there are some special

binding areas known as paratopes and idiotypes. Paratopes are structures that allow

the antibodies to identify the non-self and react correspondingly; idiotypes are

structures that allow co-stimulation between antibodies to regulate immune response.

Antibodies can circulate through the blood and lymph systems in our body to respond

with antigens in a distributed cooperative manner (Male et al. 2006). After eliminating

the pathogens, some T cells and B Cells will become memory cells for rapidly

reaction in future reencounter of same pathogens. The rapidly response is known as

the secondary response.


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2.2.2.

Artificial Immune System

The intelligent systems using this immune-based metaphor are known as Artificial

Immune Systems (AIS).

2.2.2.1.

Immune Inspired Algorithms

The following sections provide some background on three immune inspired

algorithms, namely Negative Selection theory, Clonal selection theory and Immune

network theory.

2.2.2.1.1. Negative Selection theory

To avoid the immune cell attacking other cells inside human body, the lymphocytes

need to go through a clonal deletion process before they become mature and leave the

thymus. In the clonal deletion, antigen presenting cell would present self-antigens to

lymphocytes, if the lymphocytes react to self-antigens, they would be deleted from the

body, leaving only lymphocytes which would not attack own human body. This

mechanism is called negative selection. Negative selection algorithms are inspired by

this mechanism.


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The negative selection algorithm was first proposed by Forrest et al. (1994). It is used

to detect data modifications caused by computer a virus in and malicious software.

This algorithm use a set of self-strings, S to express the normal state of a system. then

generate a set of detectors, D , which only react to the complement of S . By applying

these detectors to new, this algorithm can classify whether the data has been

manipulated. Below are the outlines of negative selection algorithm (Aitkin et al.

2013).

input : S = set of seen known self-elements

output : D = set of generated detectors

begin

repeat

Randomly generate potential detectors and place them in a set P

Determine affinity of each member of P with each member of self-set S

If any element in S recognises a detector in P with recognition threshold,

then the detector is rejected, otherwise it is added to D

until Stopping criteria has been met

end

2.2.2.1.2.

Clonal selection theory

Another popular immune inspiration mechanism is clonal selection theory. This

theory draws on the clonal expansion and affinity maturation of B-cell.


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While ensuring that only lymphocytes specific to the antigen on pathogen are cloned

and produced in large numbers, there are somatic hypermutations occur during clonal

expansion to introduce random changes to the specificity of lymphocytes, increases

the repertoire diversity. The proliferation level of the lymphocytes is directly

proportional to its affinity with the antigen it binds, and the mutation level of the

proportional is inversely proportional to this affinity.

The clonal selection inspired algorithms are often applied together with the concept

memory cells, making it useful in computational optimization and pattern recognition.

One popular clonal selection inspired algorithm is CLONALG (Cutello et al. 2005).

Below are the outlines of CLONALG algorithm (Aitkin et al. 2013).

input : S = set of patterns to be recognised,n = the number of worst elements to select for removal

output : M = set of memory detectors capable of classifying unseen patterns

begin

Create an initial random set of antibodies, A

forall patterns in S do

Determine the affinity with each antibody in A

Generate clones of a subset of A with the highest affinity.

The number of clones is proportional to its affinity

Mutate attributes of these clones to the set A , and place a copy of

the highest affinity antibodies in A into the memory set, M

Replace the n lowest affinity antibodies in A with new randomly

generated antibodies

end

end


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2.2.2.1.3.

Immune network theory

The immune network theory is proposed by Jerne (1974), which also known an

idiotypic network. This theory suggests that instead of being isolated, lymphocytes

interact with each other and form self-regulated network even in the absence of

antigens. Lymphocytes can recognize both antigens and other lymphocytes.

Figure 13 Concept of idiotypic networks

Stimulation

Suppression

Antigen

Epitope

Paratope 1

Idiotope 1

Paratope 2

Idiotope 2

Idiotope 3

Paratope 3

B-Cell 2

B-Cell 1

B-Cell 3


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The concept of idiotypic network is shown in Figure 13, where the epitope is the

antigen element and the paratope is the recognition part of lymphocytes. Idiotope is a

special part on the lymphocytes which presents the antigenic-like element of an

lymphocyte and can be recognized by other lymphocytes.

While epitope of antigen binds and matches with paratope of lymphocyte, the idiotope

lymphocyte binds and matches with paratope of other lymphocytes. In the Figure 13,

the antigen with epitope matching with B-cell’s paratope stimulates B-Cell-1. B-Cell-

1 stimulates B-Cell-2 as Paratope-2 binds to Idiotope-1. B-Cell-1 suppresses B-Cell-3

as Paratope-1 binds to Idiotope-3. These suppressions and stimulations allow the

immune system having interactions with itself even without the existence of antigen.


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The immune network theory display emergent properties, such as self-tolerance,

learning and memory. Below are the outlines of a pattern recognizing algorithm

inspired by immune network theory (Aitkin et al. 2013).

input : S = set of patterns to be recognised,

nt = network affinity threshold,

ct = clonal pool threshold,

h = number of highest affinity clones,

a = number of new antibodies to introduce

output : N = set of memory detectors capable of classifying unseen patterns

begin

Create an initial random set of network antibodies, N

repeat

forall patterns in S do

Determine the affinity with each antibody in N

Generate clones of a subset of the antibodies in N with the highest

affinity. The number of clones for an antibody is proportional

to its affinity

Mutate attributes of these clones to the set A , a and place h

number of the highest affinity clones into clonal memory set, C

Eliminate all elements of C whose affinity with the antigen is

less than a predefined threshold ct

Determine the affinity amongst all the antibodies in C

and eliminate those antibodies whose affinity with each

other is less than the threshold ct

Incorporate the remaining clones of C into N

end

Determine the affinity between each pair of antibodies in N and

eliminate all antibodies whose affinity is less than the threshold nt

Introduce a random number of randomly generated antibodies

and place into N

end until a stopping criteria has been met

end


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2.2.2.2.

Immune-Based Single Robot Control

Immune network theory is useful in robot behaviours control. Early as 90’s, Ishiguro

et al. show the possibility of controlling autonomous robot’s behaviours using

idiotypic network (Ishiguro, Kondo et al. 1995, Ishiguro, Shirai et al. 1996, Ishiguro,

Watanabe et al. 1996). The actions of robots are formulated and decided by the

concentration of antibodies. The immune network gives the system capacity to learn

and let the robot perform better decision after evolution. Figure 14 shows

photography of the practical robot (Ishiguro, Watanabe et al. 1996). The antibodies

help the robot to decide the energy level to trigger recharge and avoid over charge.

Figure 14 Immunoid robot in experiment using immune network based decision making(Ishiguro, Watanabe et al. 1996).

Similar methods applied by Vargas et al. (2003a and 2003b). These studies also show

the idiotypic network can be applied to autonomous robot navigation. The immunenetwork even can help robot to escape from dead-end trap (Luh and Liu 2008) or path

planning (Duan et al. 2004).


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2.2.2.3.

Immune-Based Multi-Agent System Control

The theoretical framework of AIS has been broadly studied in the field of Artificial

Intelligence (AI) (Dasgupta 1999, De Castro and Timmis 2002, Ji and Dasgupta 2003,

Ishida 2004, Timmis 2007, Timmis et al. 2008). Several studies (Dasgupta 1998, Lau

and Wong 2003, Ko et al. 2004, Lau and Wong 2004, Lau and Wong 2006, Lu and

Lau 2009) concerning distributed multi-agent system control have been carried out

previously and shown the AIS-based control framework having desirable performance

and flexibility for multi-agent coordination. Although the focus of those studies were

not on failure prevention of MAS, they did provide inspiration and interesting ideas

for developing AIS based MAS control.

Being one of the multi-agent systems, multi- robot systems take advantages of on the

immune-based control. The follow sections are some related immune-based control

frameworks and immune inspired robot related applications.

In 1998, Dasgupta (1998) proposed a general framework for multi-agent decision

support, which is the one of the first frameworks for building MAS with

immunological design. In his design, three immunological properties: 1) mobility, 2)

adaptively and 3) collaboration were introduced to the system, and agents were

classified into different behavioural groups with respect to their functions (Beer et al.

1990).


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Agent Environment

Agent Communication Scheme

Agent Control Mechanism

Response / Decision Strategies

Figure 15 Functional Modules Architecture by Dasgupta (1998)

Figure 15 shows the functional modules architecture of the general framework

proposed by Dasgupta, where response and decision strategies were made on top ofthe agent environment by a set of agent communication scheme and agent control

mechanism. This framework helps to achieve better interaction and communication

through co-simulations between role-based agents.


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In 2004, Ko et al. (2004) presented a general suppression control framework (GSCF)

to control a decentralized mechatronic manipulator arm of modular. The framework is

mainly inspired from the immune suppression hypothesis in the immune

discrimination theory (Sompayrac 1999).

Figure 16 The General Suppression Control Framework by Ko et al. (2004)

Figure 16 shows the design of general suppression control framework where emergent

group behaviours are generated by suppression mechanism between aggressive and

tolerant cells according to the local environment changes. This framework helps

effectively reduce the counter-productive behaviour committed by individual modular

parts of the robot arm and also helps in self-balancing robot control (Ko et al. 2005).


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Lau & Wong (2003, 2004, and 2006) developed a distributed multi-agent control

framework to effectively control a group of agents with different capabilities based on

a number of immune system characteristics such as immune memory (Smith et al.

1999), specificity (Hofmeyr and Forrest 1999) and diversity (Fukuda et al. 1999).

Figure 17 Architecture of control framework for individual AIS agents by Lau (2006)

Figure 17 shows the architecture of the distributed multi-agent control framework

where individual AIS agents are able to adapt their pre-defined functionalities and

generate new functionalities based on their own long terms and short terms memory.

This framework has shown significant improvement on the efficiency of MAS when

applying in automated material handling (Lau and Wong 2003) and intelligent

transport system (Lau and Wong 2004).


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Lu & Lau (2009) created a real-time cooperative control framework for networked

multi-agent systems, namely AIS-based Cooperative Reaction Framework (ACRF).

This framework was mainly inspired from the lymphocyte repertoires’ meta-dynamics

(Mehr 2005) and the immune diversity (Tonegawa 1983). A preliminary simulation

studies has been done demonstrating the effectiveness of ACRF for controlling agent

team in RobotFlag Drill problem.

There are also other immunity based multi-robot research studies focusing on specific

applications, such as, Lee & Sim (1997) use clonal selection and immune network to

control a Distributed Autonomous Robotic System (DARS) in solving search problem;

Meshref & Landingham (2000) use immune network to solve a Dog and Sheep

problem; and the hybrid algorithm Thayer and Signh (2002) used for robot

exploration and mapping domains. These all shows immune network useful in multi-

robot system.

Though those research studies mentioned above are not directly related to robot

failure, except Khan & Silva (2008) start suggest an immune network based self-

regulated fault tolerant multi-robot system with some consideration on partial and full

failure robot. Yet, in their approach, there are only job reallocations when robot

failures occur, without having consideration on robot recovery.


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2.3.

Summary

In this chapter, a review on the development of robot recovery and a summary of

related recovery mechanism is presented in Section 2.1. These studies help to move

towards automatic recovery in multi-robot system. Although some studies are still in

rudimentary stage, with increasing numbers of research studies focusing on this area,

more mechanisms will be available and the recovery technology will soon be mature.

However, those research studies yet look into the effectiveness of automatic

cooperative failure recovery, such as the worthiness of having one of the robots

actually helps in the action of recovering other failed robots. It would be worthwhile

to create a generic automatic cooperative failure recovery model which can coordinate

and provide effective and dynamic control for those mechanisms.

The reviews on artificial immunity in Section 2.2 demonstrated the capability,

principles and properties of AIS on current applications, which can be applied to the

proposed new recovery model.


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Chapter 3

Cooperative Recovery Model

One of the most important concepts in this model is the introduction of cooperative

failure recovery, the concept of allowing a team of robots to recover and survive in

situation which individual cannot. By combining the capability of multi-robot, this

model allowing the system to continue functioning effectively under dangerous and

poor environment. The implementation gives the system ability to recover from

problem, which beyond the capability of single robot can do. It is thought-provoking

to study the influence of cooperative recovery on a multi-robot system and how can it

affect the system in terms of system productivity, survivability and overhead. This

chapter is going to look into the concept of cooperative failure recovery, its

implication as well as the proposed model which helps to control the recovery action

while adapt the system situation and optimise the overall performance.

The cooperative recovery model proposed in this chapter also takes account of robot

self-recovery. The model can be applies to the most of common physical failures of

robot system. Yet, software failures are not its primary concern.

3.1. Cooperative Failure Recovery

The cooperative failure recovery itself is a generic concept which refers to any

operations that the robots may performs on the spot to assist malfunctioned robots

recovering from non-functional status to a better functional status. The cooperative

failure recovery operations diverse among systems and the degree of recovery can be

varied.


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For example, if one robot in a landmine removal system accidently has its wheel

rolled on a mud trap or being locked up for some reasons becoming immobilized; the

operations which other robots may performs freeing it from the trap, such as pulling

or pushing, resulting the immobilized robot can able to continue its reconnaissance

mission, would be considered as cooperative failure recovery operations. If the

problem is more complicated and the robot still cannot move after leaving the trap,

having other robots withdrawing it to the safe area in repairing station can also be

considered as a cooperative failure recovery operation.

The goal of the concept is to give the system cooperative recovery abilities which can

help the system keep functioning for a longer period and are considered much useful

for system deployed in the highly hazardous environments, such as highly radioactive

area, where human intervention on the spot are not favourable.

3.2. Recoverable Mechanics

There are many ways to implement a cooperative recovery solution, and the

cooperative recovery concept can be applicable in numerous situations. Yet, the detail

implementation of recovery mechanics for each particular robotic failure can be very

specific. As the focus on this study is on the general impact of cooperative recovery to

the multi-robot system, this thesis would not cover the mechanical details of each

recovery method. A collection of possible robot recovery mechanics has already

covered in Chapter 2. Those mechanics can be summarised into three implementationapproaches when developing this model. The recovery operations in this research are

assumed to be implemented on robot system which built with these three design

approaches: 1) parts replacing, 2), strength combining, and 3) resources sharing

In this basic model, only the first two approaches are considered. The resources

sharing approach will be introduce in Chapter 4.


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3.2.1.

Parts Replacing

This approach allows some failed components and parts on a robot being

replaced or reconfigured by its own or other robots. Spared parts installed on

the robot; or a robot can obtain and replace some components from others.

Examples of “Parts Replacing” design are mining robots with spare drill for

changes; surveillance robots with spare tire and wheels. Those modular

reconfigurable robots and other models in section 2.1.2.2 with abilities to swap

robot module are also belong to this type.

With this “Parts Replacing” design, robots can carry out cooperative failure

recovery operations by replacing the malfunctioning components on failed

robots.

3.2.2. Strength Combining

The “Strength Combining” approach allowing robot to be lifted up by another

robot or being towed to repair station. Multiple robots may combine strength

to remove obstacles in order to free those robots being trapped. The inter-robot

docking in section 2.1.2.4 can be useful for this approach.

This design enables the cooperative failure recovery operations by having

assistance from other robots. Problems apart from location trapping are all

assumed to be fixed automatically or with manual in the repair station where is

the location human interaction is allowed in the environment.

Given huge effort to develop and invest on the implementations of these mechanism,

one multi-robots system with all those three recoverable mechanics would be consider

as an advanced tolerant multi-robot system with very high survivability in different

situations.


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3.3.

Different roles of robots

For the purpose of this thesis, the robots are labelled as “red” robot, “white” robot and

“black” robot. These are the three roles of robots in the recovery model, their roles

change according to their status in the system.

Table 3. Colour of role in particular roles for illustration purpose

Colour Role and Status

Red Productive, carrying out system primary objective

White Non-productive, Helps in robot recoveryBlack Non-productive, malfunctioning, failed, need rescue

Table 3 listed how the colour is used to label the corresponding role of robot, which

will help explaining the proposed model and robot interaction in the following

chapters. “Red” robots refer to the robots working on the primary system function;

“Black” robots refer to the robots malfunctioning; while “White” robots refer to therobots which carrying out recovery operations.

To maximize the system suitability, in this basic model, only red robots can become

white robot and the cooperative recovery can only be carried out by white robot.

Figure 18 illustrates the change of robot from different state.


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Figure 18 The change in robot state and role

White

Robot

Red Robot

Black

Robot

Helps in robot

recovery

Malfunctioning,

Failed,

need rescue

Recovered /repaired


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3.4.

The Cooperative Recovery Trade-off Problem

Although cooperative failure recovery can be favourable to many multi-robot systems

in principle, how to make the operations effective is one big issue. One of the

intentions in this research is to find out whether it is worthwhile to develop those

mechanics and whether a system with cooperative recovery can be cost-effective after

all.

The process of cooperative failure recovery itself does not favourable to the primary

system objective. Whenever a robot turns into a “white” robot, it has to stop

processing primary system tasks. This leads to a temperate drop in system functioning

performance until it turns to “red” again. One typical situation is having the entire

group of functioning robot turns into “white” robots, leaving no “red” robots

performing primary functions. Figure 19 shows two troughs in the primary objective

score of a system due to cooperative recovery operations.

The system performance would greatly suffer if the priority of the recovery action is

set too high. However, the priority of recovery action cannot be too low neither,

otherwise, there will chance where all robots become “black” and having entire

system failure.

Therefore, there must be a means to control the entire cooperative recovery operations,

such that the system recovery mechanics can be worthwhile.


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Figure 19 The performance of system drops when recovery operations in place

Here are the problems the system should be addressing from time to time:

1. Which cooperative recovery operation should be carried out?

2. When the operations should be done? What is the priority?

3. Which robots should be the “white” robot?

4. Would the operations put the system survivability at risk?

5. How would the primary system function being affected?

6. Did the operations really resolve the problem?

A new recovery model inspired by human immunity system is hence proposed to

address this captioned issue.

Time

2 black robot

+ 3 white robot invloed in

operation

1 black robot

+ 1 white robot

invloed in operation

P r i m a r y O b j e c t i v e S

c o r e


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3.5.

Immunity Inspired Recovery Model

In this proposed model, all actions ( ) can be performed by each robot are consideredas two types: one is the Normal Actions (), which is the primary action that therobots must carry out in order to fulfil its original purpose; the other is the

Sustainment Actions (s), which the robot can performs to maximise overall system life,

including all recovery strategy. Every robot in the system is assumed to have the

abilities to perform one of these actions at a time.

The requirements and designs for each cooperative failure recovery strategy can be

very different, it would, however, not affect the application of this generic model.

Sustainment Actions () are defined as all the behaviours that are used to preserve andimprove the system sustainability, such that the system can operate, sustain and keep

on productive for a longer period. Failure Recovery Actions ( ) and the Mandatory Maintenance Operations ( ) are both belongs toSustainment Actions. A strategic sustainment action series is defined as sustainment

response () and strategic normal action series is defined as normal responses ().


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3.5.1.

A Two-level Response Architecture

Inspired by two-line defence mechanism of human immune systems (Sompayrac 1999,

Purves et al. 2001, Male et al. 2006), there are two levels of sustainment response:

Self-Sustainment Response () and Cooperative Sustainment Response (). Thedesign of the two-level sustainment responses shares the same characteristics with the

innate and adaptive response in human immune systems. These two responses form

an essential protection for multi-robot system to recover from different degrees of

failure and help to preserve a balance between system performance and system

sustainability.

In the two levels sustainment model, the Self-Sustainment Response () refers to allgeneral sustainment actions which individual robot can performs to make precaution

and prevent itself failing, such as essential maintenance and replacing of replaceable

components; while Cooperative Sustainment Response () refers to all sustainmentactions which individual robot can performs to recovery failure robots and prevent

entire system failure.

The primary reason of having two levels architecture of responses is to avoid common

robot failures if possible and minimize the chance of triggering cooperative response.

Although the cooperative sustainment response is useful to maintain system

sustainability, it is however not very favourable to the system performance due to its

implantations are rather specific and usually more time consuming. This architecturehelps to control the trade-offs.


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Innate Immune

Response

Adaptive Immune

Response

Self-Sustaining

Response (R ss)

Cooperative Sustaining

Response (R sc)

Human Immune System :

Proposed Recovery Model :

1° line of defence 2

° line of defence

If pathogens

evade

If failures

occur

Figure 20 The two-level defence mechanism in human immune system and proposed model

Figure 20 shows how the two-level sustainment response model was inspired from

human immune system. In human immune system, innate immune response functions

as the first general barrier which attempts to stop pathogens from entering the body,

helps minimizing the chance of infections; while adaptive immune response triggers if

pathogens escape from innate response, it produces specify antibodies to stop

infections (Purves et al. 2001). In this new model, the two-level sustainment

responses work in a similar manner. The Self-sustainment response () works as thefirst barrier to reduce the chance for common robot failure while cooperative

sustainment response () activates when there robot failures occurred and helps thesystem to recover.


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Table 4. Relationships between the proposed recovery model and the immune system

Immune System Proposed Recovery Framework

Self Red robots, White robots

Non-self Tasks, Black robot

Anti-bodyPair of Conditions with Strategic Behaviours

(Response)

Antigen Set of Conditions (Conception)

Antigen Presenting Cell (APC) Sustainability Evaluator

Innate Response Self-Sustainment Behaviour

Adaptive Response Cooperative Sustainment Behaviour

Co-Stimulus Relations between Strategic Behaviours

Table 4 shows the mappings between immune system and the proposed recovery

model. The “Red” Robot and “White” Robot are regarding as self, which is good and

no harm to the system; while tasks and “Black” robot are r egarding as non-self, which

should be clear and reduced whenever possible. System conditions are the antigen; the

strategic behaviours the anti-body which trying to match with the system condition.

Sustainability evaluator serves as the antigen presenting cell, which evaluate the latest

system conditions. The relations between behaviours are control through the

suppression and stimulation mechanism inspired by immune network theory (Jerne

1974).


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3.5.2.

The Control System

Design of the control system of individual robot is shown in Figure 21. The functions

of each component are described as follows:

1. Sustainability Evaluator

During system operation, the condition of the system situation is perceived as

perceptions through different detectors and sensors. The functions of the

Sustainability Evaluator (APC) is to evaluate the current system situation base

on the perceptions, creates the conception (antigen) of the current system

condition and present it to the Response Reactor.

2. Response Reactor

The Response Reactor processes the condition and tries matching the

condition with potential behaviours (antibodies). Its functions are to calculate

the favourability of the potential behaviour, mutating their attributes and find

out the most suitable candidate. Suppression and stimulation among

behaviours also take place inside the Response Reactor.

3. Response Evaluator

In general, the behaviours with the highest favourability would be carried out

by the robot. The response evaluator is use to justify if the behaviours

favourable as expected, which give feedback and adjust the potential

behaviours in the Response Reactor for next decision.

4. System Environment

The system environment is where the interactions between robots take place.


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Figure 21 Flow of operation from perception to behaviours

Response

Reactor

Sustainability

Evaluator

Response

Evaluator

System Environment

Perceptions

(Detected information)

Conceptions

(System condition)

Selected Behaviors

Result of Behaviors

Feedback


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3.5.3.

Design of Antibodies

There are two types of anti-body, the Normal Response Antibody, N-Cell, and

Sustainment Response Antibody, S-Cell. The N-Cells are corresponding to normal

actions, responsible for the primary system objectives; while the S-Cells are

corresponding sustainment actions, responsible for the system sustainability. The

structures on these antibodies are shown in Figure 22.

Conceptions

Paratope

Normal Response Co-stimulus

Idiotype

N

Cell

- Detection of Obstacles

- Detection of Tasks

- Concentration of Agents- Distance from stations- Signal Strength

- Type of Signal- etc …..

- Approaching Tasks

- Avoid Obstacles

- Continue Explore- Completing Tasks

- Response Attributes

Conceptions

Paratope

Sustainment Response Co-stimulus

Idiotype

S

Cell

- Existence of Failed Agent

- Concentration of Agents- Agent Health Level- Location of Failed Agent

- Distance from Base- Sustainment Index

- Self Sustain Level- Cooperative Sustain Level

- Self Sustain Action

Sets- Cooperate Sustain

Action Sets

- Response Attributes

- Suppressed

N Cell Actions- Suppressed

S Cell Actions- Stimulated

N Cell Actions

-Suppressed

N Cell Actions- Stimulated

N Cell Actions

Normal

Response

Antibod

Sustainment

ResponseAntibody

Function

- Productive

Function

Function

- Cost Function

- Recovery

Function

Figure 22 Design of N-Cell and S-Cell

Each antibody corresponds to a particular action or response. The set of conditions

triggering the antibody are modelled as paratopes, which are the required system

conditions to carry the corresponding action or response. The size of paratopes and

the conditions are vary depending on the system objectives and the recovery

mechanics applied. If the response is to replace the damaged drill by obtaining a spare

drill from nearest robot, having one robot with spare drill nearby would be one of the

condition in the paratope. In order to trigger an antibody, all the conditions of

paratope must be matched.


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The middle parts of the antibody are the action response action series, which is the

actually action the robot have to involve in the response and the related attitudes, such

as direction, amount and type of components to be involved. The body of the antibody

also contains the favourability functions for that response, which describe the way of

how the favourability of that antibody being formulated.

Some actions or response may have special relationship to others. For example, if a

robot is low in health and the nearest repair station is at the east, moving to carry out

tasks at east would be more favourable than moving to west for other tasks. Similarly,

if a robot having communication problem with its radio receiver, moving toward other

robot and keeping line of sight distance would be more favourable than moving to

unexplored area. These relations are modelled as the idiotype for stimulating or

suppressing related antibodies.

To conform to the two levels defence mechanism, the S-Cell carrying Self-

Sustainment Response would suppress those carrying Cooperative Sustainment

Response; and S-Cell would suppress N-Cell in general.


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3.5.4.

Classification of Recovery Operations

Based on the complexity and the effectiveness of each recovery operation, each

recovery operation can be classified by four criteria.

1) Would the operation put the “white” robot at risk?

2) Would the operation involve more than one “white” robot?

3) Would the operation fully resolve the malfunction for a reasonable period?

4) Would the operation put the “white” robot at risk?

If the operation involves only itself, it is denoted as “Self recovery”; if it involves

only one “white” robot, it is denoted as “Simple” recovery; on the other hand,

“Complex” recovery refers to recovery operations which involve multiple “white”

robots. If an operation would be able to completely resolve the problem for a

reasonable long period, it would be a “Full” recovery, else, it denoted as “Partial”

recovery. If the operation would not introduce potential problem to the “white” robot,

the operation would denoted as an “Assist” recovery operation, otherwise, which

would be denoted as “Sacrifice” recovery. “Sacrifice” recovery operation would not

be consider in this basic model. This will be discussed and introduce in chapter 4

inside the Sacrificing Recovery Model.


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3.5.5.

Behavioural Decision Making Algorithm

Inside the Response Reactor, a decision making algorithm shown as below is applied.

Input : T = System condition from Sustainability Evaluator

P = processing power of individual robot

Sr = sustainability ratio,

h = number of highest favourability clones

ft = favourable threshold

Output : B = highest favourable antibody

Begin

Create a set of p randomly selected antibodies from Rs and Rn, P

The numbers of antibodies from O and N are proportional to Sr

forall behavioural decision making do

Eliminate antibodies not matching with antigen T from P

Generate clones of the antibodies in P, A

Mutate attributes of these clones A

Determine the favourability amongst all the antibodies

Eliminate the antibodies with favourability lower than ft

If antibodies remains in P

execute highest favourable antibody, B

increase favourable threshold ft value

otherwise

reduce value of favourable threshold ft

repeat regenerating randomly selected antibodies step

Get feedback from the Response Evaluator

If returns less than ft,

place a copy of the B into

the unfavourable memory set, UM

eliminate B from P

otherwise place B into favourable memory set, FM

place the h highest

favourability antibodies into the P

Introduce randomly selected antibodies from Rs and Rn

and place into P following the ratio Sr

end

end


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The algorithm is built upon the emergent learning and memory properties of the

immune network theory, while having a scalable consideration on the processing

power limit of individual robots. As the complexity of system and recovery

mechanics increase, the number of antibodies would increase in scale. Indeed of

considering all possible antibodies in every behavioural decision, a sample population

is used instead. The population size (p) can be setup according to the actual

processing power of individual robot. A sustainability ratio () is used to control theamount of normal response and sustainment response to be considered at a time.

The ratio of N-cell and S-cell is defined as sustainability ratio () computed based onthe sustainability level of the robot. Equation (1) shows the formula of the ratio,

where n is the number of N-cell being selected while s is the number of S-cell being

selected.

(1)

The sustainability level () of a robot is determined by the its estimated healthstatus of the robot and the ratio of functioning robots around at that particular moment,

which is show as equation (2) below, where .

(2)

is the estimated health of the robot and the is the maximum possible healthof the robot . The total number of robot in the system is denoted by and refersto the number of functioning robots. When the sustainability level of a robot is high,

the value approaches 1 and it drops as sustainability drops.

(3)


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In this model, the sustainability level ( ) is equal to sustainability ratio ( )multiplied by constant justifier , depending on the importance of systemsustainability. The justifier define the minimum percentage of S-cell response would

be taken into consideration every decision circle. The smaller it is the higher

percentage of S-cell would be selected for decision making. If is equals to three,there will be at least 25% of decision antibodies taken from S-cell; if equals to one,the minimum percentage of S-cell for consideration would be 50%. In a goal-

orientated system, such as rescue robots in disaster, where rescue mission itself is

more important than the life of robot, the justifier would be bigger. In planetary

exploration system, which system sustainability is crucial, the justifier would be

smaller for maximizing the system survivability.

The favourability () of an antibody x is determined by the favourability function ofthe antibodies. For N-cells, the favourability function is defined as equation (4), its

value is affected by the expected productive value ( for completing thecorresponding normal response, number of favourable memory ( ) andunfavourable memory () of robot decision and the influence from other triggeredantibodies (). Equation (5) shows the influence value where the first term isthe stimulation from other antibodies and the second term is the suppression among

antibodies. denoted as the stimulation from antibody y to antibody x, denotedas the suppression of antibody z to antibody x.

(4)

∑ ∑ (5)


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The favourability function of S-cell is defined as equation (6), the favourability is

determined by the expected gain in production value after the response ( ), theexpected loss of production during the recovery (), the current sustainability level(

) of the robot, as well as memory and co-stimulations factors.

(6)

The favourable memory ( ) and unfavourable memory () give merits to theantibodies which bring benefits to the system and penalties to those not performingwell as expected. Th

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