Morphologically Responsive Self-Assembling Robots
by
Rehan O’Grady
——–
Universit´e Libre de Bruxelles
Facult´e des Sciences Appliqu´ees, CoDE, IRIDIA rogrady@ulb.ac.be
——–
Supervised by
Marco Dorigo, Ph.D.
——–
Directeur de Recherches du FNRS Universit´e Libre de Bruxelles
Facult´e des Sciences Appliqu´ees, CoDE, IRIDIA mdorigo@ulb.ac.be
——–
June, 2010
A thesis submitted in partial fulfilment of the requirements of theUniversit´e Libre de Bruxelles,Facult´e des Sciences Appliqu´ees for the doctoral degree (PhD) in
Sciences de l’Ing´enieur
Abstract
We investigate the use of self-assembly in a robotic system as a means of responding to different environmental contingencies. Self-assembly is the mechanism through which agents in a multi-robot system autonomously form connections with one another to cre- ate larger composite robotic entities. Initially, we consider a simple response mechanism that uses stochastic self-assembly without any explicit control over the resulting morphol- ogy — the robots self-assemble into a larger, randomly shaped composite entity if the task they encounter is beyond the physical capabilities of a single robot. We present dis- tributed behavioural control that enables a group of robots to make this collective decision about when and if to self-assemble in the context of a hill crossing task. In a series of real-world experiments, we analyse the effect of different distributed timing and decision strategies on system performance. Outside of a task execution context, we present fully decentralised behavioural control capable of creating periodically repeating global mor- phologies. We then show how arbitrary morphologies can be generated by abstracting our behavioural control into a morphology control language and adding symbolic communi- cation between connected agents. Finally, we integrate our earlier distributed response mechanism into the morphology control language. We run simulated and real-world ex- periments to demonstrate a self-assembling robotic system that can respond to varying environmental contingencies by forming different appropriate morphologies.
Acknowledgments
First and foremost, I would like to thank my supervisor Marco Dorigo. I am very grateful for the opportunity he gave me to work at IRIDIA. I feel that my time in this laboratory has allowed me to grow as a scientist and as a person. Marco leads by example, and from him I have learnt much more than robotics. I would also like to thank Roderich Gross and Anders Lyhne Christensen for their close collaboration. More recently, working with Carlo Pinciroli, Nithin Mathews, Mauro Birattari and Arne Brutschy has also been a pleasure. In general, I am grateful to all of my colleagues at Iridia for always being so helpful and for making IRIDIA such a pleasant working environment. Particular thanks go to Hugues Bersini, who helped to create and nurture this environment. I would also like to acknowledge the essential role played by Francesco Mondada’s group at the EPFL, who designed and built the robotic platform used in this thesis. Finally, I would like to thank Elena for her support and for not getting too upset (most of the time) about the many late nights spent in the intimate company of robots.
Contents
1 Introduction 1
1.1 Goals of This Thesis . . . 2
1.2 Scientific Contribution of This Thesis . . . 3
1.3 Other Scientific Contributions . . . 5
1.4 Publication Summary . . . 6
1.5 Thesis Structure . . . 7
2 Context and Related Work 9 2.1 The Principles of Swarm Robotics . . . 9
2.1.1 Self-Organisation . . . 9
2.1.2 Inspiration From Nature . . . 10
2.1.2.1 Self-Assembly . . . 10
2.1.2.2 Group Transport . . . 11
2.1.2.3 Aggregation . . . 12
2.1.3 Characteristics of Swarm Robotics Systems . . . 12
2.2 Self-assembling Robotic Systems . . . 13
2.2.1 Self-propelled Systems . . . 13
2.2.2 Externally Propelled Systems . . . 14
2.3 Morphology Control in Distributed Robotic Systems . . . 15
2.3.1 Morphology Control Algorithms . . . 16
2.3.2 Modular Reconfigurable Robotic Systems . . . 18
2.4 Task Execution . . . 20
2.4.1 Coordinated Movement and Rough Terrain Navigation . . . 20
2.4.2 Object Transport and Manipulation . . . 21
3 The Swarm-Bot Robotic Platform 25 3.1 Physical Characteristics . . . 25
3.2 CPU, Control Electronics and Software . . . 25
3.3 Actuation . . . 26
3.3.1 Gripping . . . 26
3.4 Sensing . . . 28
3.4.1 Image Processing with the Camera . . . 28
3.4.1.1 Coloured Object Detection . . . 29
3.4.1.2 Target Direction Noise Filtering . . . 30
3.4.2 Hill Magnitude and Orientation Detection with Inclinometers . . . . 31
3.5 Behaviour Development Environment . . . 31
3.5.1 Common Control Interface . . . 31
3.5.2 Twodee Simulator . . . 31 V
3.5.3 Behavioural Control Principles . . . 32
4 Decisional Autonomy 33 4.1 Experimental Setup . . . 34
4.1.1 The Environment . . . 34
4.1.2 The Task . . . 34
4.2 Distributed Behavioural Control . . . 36
4.3 Results . . . 42
4.3.1 Trials with 3 s-bots in Environment A . . . 42
4.3.2 Trials with a single s-bot in Environment B . . . 43
4.3.3 Trials with 2 s-bots in Environment B . . . 43
4.3.4 Trials with 3 s-bots in Environment B . . . 43
4.4 Analysis . . . 44
4.4.1 Success Rate . . . 44
4.4.2 Timing Analysis of 2-s-bot Trials in Environment B . . . 45
4.4.3 Timing Analysis of 3-s-bot Trials in Environment B . . . 47
4.4.4 Behavioural Analysis of a Single 3 s-bot Trial (trial 16) . . . 49
4.5 Discussion and Conclusions . . . 51
5 The Value of Self-Assembly 53 5.1 Experimental Setup . . . 53
5.2 The Basic Self-Assembly Response Strategy . . . 54
5.2.1 Strategy Implementation for the Hill Crossing Task . . . 54
5.3 Benefits of Scale . . . 57
5.3.1 TheIndependent Execution Only strategy . . . 57
5.3.2 Results . . . 57
5.4 Benefits of Decisional Autonomy . . . 59
5.4.1 ThePreemptive Self-Assembly Strategy . . . 59
5.4.2 Results: Validation of the Response Mechanism . . . 59
5.4.3 Results: Benefits of Decisional Autonomy . . . 59
5.5 The Connected Coordination Strategy . . . 61
5.5.1 Strategy Implementation for the Hill Crossing Task . . . 61
5.6 Benefits of Connected Coordination . . . 64
5.6.1 Results . . . 64
5.7 Scalability . . . 65
5.8 Basic Self-Assembly Response strategy in a Hole Crossing Task . . . 66
5.9 Discussion and Conclusions . . . 68
6 Resource Allocation 71 6.1 Experimental Set-up . . . 72
6.2 Distributed Behavioural Control . . . 72
6.3 Results . . . 74
6.3.1 Rescuing Broken Robots in Parallel . . . 74
6.3.2 Physical Cooperation to Rescue a Broken Robot . . . 74
6.3.3 Efficiency Gains Through Group Size Regulation . . . 75
6.3.4 Reallocation of Resources in a Deadlock Situation . . . 75
6.4 Discussion and Conclusions . . . 77 VI
7 Directional Self-Assembly 79
7.1 Methodology . . . 79
7.2 The Connection Slot . . . 80
7.3 Behavioural Control . . . 80
7.3.1 Approaching and Gripping a Connection Slot . . . 81
7.3.2 Finding and Navigating to a Connection Slot . . . 83
7.4 Experimental Results: Precision and Timing . . . 84
7.5 Discussion and Conclusions . . . 85
8 Distributed Morphology Growth 87 8.1 Methodology . . . 88
8.2 Morphology Extension Rules . . . 89
8.3 Experiments: Morphologies With Real Robots . . . 91
8.3.1 The Four Morphologies . . . 93
8.3.1.1 Line Morphology . . . 93
8.3.1.2 Balanced Star Morphology . . . 93
8.3.1.3 Balanced Arrow Morphology . . . 94
8.3.1.4 Rectangle Morphology . . . 94
8.3.2 Results . . . 95
8.3.2.1 Timing . . . 95
8.4 Experiments: Scalability . . . 97
8.4.1 Simulation Verisimilitude . . . 97
8.4.2 Scalability Performance . . . 98
8.5 Discussion and Conclusions . . . 99
9 Scripted Generation of Arbitrary Morphologies 101 9.1 Methodology . . . 102
9.2 Behavioural Control with Swarmorph-script . . . 102
9.2.1 Directional Self-Assembly . . . 103
9.2.1.1 Instructions: InviteConnection, FindSlotThenConnect. 103 9.2.2 Communication and Control Flow . . . 103
9.2.2.1 Communication Instructions: SendInstrSeqId, ReceiveInstrSeqId . . . 103
9.2.2.2 Control Flow Instructions: if, then, end, StopExecution . . . 104
9.2.2.3 Generic Script Structure using Communication and Con- trol Flow . . . 104
9.2.3 Homogeneous Behavioural Control through Obstacle based Seeding . 106 9.2.3.1 Instruction: RandomWalkUntil . . . 106
9.2.3.2 Generic Script Structure using Obstacle Based Seeding . . 106
9.2.4 Results . . . 107
9.3 Multiple Morphologies and Reconfiguration . . . 108
9.3.1 Morphology Splitting to Generate Multiple Morphologies . . . 110
9.3.1.1 Instructions: Disconnect, Retreat . . . 110
9.3.2 Results: Multiple Morphologies . . . 110
9.3.3 Reconfiguration . . . 111
9.3.3.1 Instructions: SendSignal, PauseUntilSignal . . . 113
9.3.3.2 Example Script: Reconfiguration . . . 113
9.3.4 Results: Reconfiguration . . . 114 VII
9.4 Discussion and Conclusions . . . 117
10 Morphologically Responsive Self-Assembling Robots 119 10.1 Methodology . . . 119
10.2 Tasks and Morphologies . . . 120
10.2.1 The Gap Crossing Task . . . 120
10.2.2 The Bridge Traversal Task . . . 121
10.2.3 The Object Pushing Task . . . 121
10.3 Behavioural Control . . . 121
10.3.1 Additional Swarmorph-script Commands . . . 122
10.3.1.1 Navigation Instructions: IndividualPhototaxisUntil, ConnectedPhototaxisUntil . . . 122
10.3.1.2 Control Flow Instructions: Label, Jump . . . 122
10.3.2 Example script: Respond to gap with a line morphology . . . 122
10.3.3 Behavioural Control . . . 124
10.4 Results: Simulation Based Experiments . . . 126
10.4.1 Basic Task Execution . . . 126
10.4.2 Negative Influence of Interference . . . 127
10.4.3 Scalability . . . 127
10.5 Results: Real World Experiments . . . 128
10.6 Discussions and Conclusions . . . 131
11 Discussion and Conclusions 133 11.1 Ongoing and Future Work . . . 133
11.1.1 Behavioural Control Logic Transmission . . . 133
11.1.1.1 Script Communication Instructions SendScript( [encoded instruction sequence] | ‘self’ ), ReceiveScript(), ExecuteReceivedScript() . . . 134
11.1.1.2 Results: Morphologies Generated using Behavioural Con- trol Logic Transmission . . . 134
11.1.2 Morphology Control in a Heterogeneous Swarm . . . 137
11.1.3 Other Possible Research Directions . . . 138
11.2 Conclusions . . . 138
VIII
