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Immune-Inspired Self-Healing Swarm Robotic Systems Amelia

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Immune-Inspired Self-Healing Swarm Robotic Systems Amelia Ritahani Ismail Submitted for the degree of Doctor of Philosophy University of York Department of Computer Science September 2011 Abstract The field of artificial immune system (AIS) is an example of biologically inspired comput- ing that takes its inspiration from various aspects of immunology. Techniques from AIS have been applied in solving many different problems such as classification, optimisation and anomaly detection. However, despite the apparent success of the AIS approach, the unique advantages of AIS over and above other computational intelligence approaches are not clear. In order to address this, AIS practitioners need to carefully consider the application area and design methodologies that they adopt. It has been argued that of increasing importance is the development of a greater understanding of the underlying immunological system that acts as inspiration, as well as the understanding of the prob- lem that need to be solved before proposing the immune-inspired solution to solve the desired problem. This thesis therefore aims to pursue a more principled approach for the development of an AIS, considering the application areas that are suitable based on the underlying biological system under study, as well as the engineering problems that needs to be solved. This directs us to recognise a methodology for developing AIS that integrates several explicit modelling phases to extract the key features of the biological system. An analysis of the immunological literature acknowledges our immune inspira- tion: granuloma formation, which represents a chronic inflammatory reaction initiated by various infectious and non-infectious agents. Our first step in developing an AIS sup- ported by these properties is to construct an Unified Modelling Language (UML) model agent-based simulation to understand the underlying properties of granuloma formation. Based on the model and simulation, we then investigate the development of granuloma formation, based on the interactions of different signalling mechanisms and the recruit- ment of different cells in the system. Using the insight gained from these investigation, we construct a design principles to be incorporated into AIS algorithm development. The design principles are then instantiated for a self-healing algorithm for swarm robotic sys- tems, specifically in the case of swarm beacon taxis when there exist failure of robots’ energy in the systems. The self-healing algorithm, which is inspired by the granuloma formation of immune systems is then tested in swarm robotics simulation. To conclude, we analyse the process we have pursued to develop our AIS and evaluate the advantages and the disadvantages of the approach that we have taken, showing how a more principled approach with careful consideration the application area can be applied to the design of biologically-inspired algorithms. 1 Contents 1 Introduction 21 1.1 Motivation . 21 1.1.1 Biologically-Inspired Computation . 22 1.1.2 Artificial Immune Systems . 24 1.2 Swarm Robotics . 24 1.3 Outline of Thesis . 26 1.3.1 Research Goal and Contributions . 26 1.3.2 Thesis Structure . 27 1.3.3 Publications . 29 2 Swarm Robotics 31 2.1 Swarm Intelligence . 32 2.2 Swarm Robotics and Its Applications . 35 2.2.1 Challenges and Issues . 40 2.3 Swarm Taxis Algorithms . 41 2.3.1 α-algorithm . 44 2.3.2 β-algorithm . 47 2.3.3 !-algorithm . 49 2.4 Swarm Intelligence Approaches to Swarm Robotics . 50 2.5 Conclusions . 52 3 Artificial Immune Systems 55 3.1 Artificial Immune Systems and Its Algorithms . 56 3.1.1 Swarm-like Immune-inspired Algorithms . 57 2 3.1.2 Non-swarm Immune-Inspired Algorithms . 58 3.2 Immune Algorithms in Swarm Robotics . 59 3.3 Approaches in Developing Immune-inspired Swarm Robotic Systems . 62 3.4 Modelling and Simulation of the Immune Systems . 65 3.4.1 Diagrammatic and Software Engineering Approaches . 69 3.4.2 Agent-based Simulation for Biological System . 74 3.4.3 Engineering-Informed Modelling Approach . 76 3.5 Granuloma Formation as an Inspirations for Swarm Robotic Systems . 77 3.5.1 Biological Background of Granuloma Formation . 78 3.5.2 Cells and Development of Granuloma Formation . 79 3.5.3 Mapping from Granuloma Formation to Swarm of Robots . 82 3.6 Conclusions . 85 4 A Model and Simulation of Granuloma Formation 87 4.1 Research Context . 88 4.2 Domain Model . 89 4.2.1 Modelling the Expected Behaviours . 93 4.2.2 Modelling Low-level Dynamics of Cells . 93 4.3 Platform Model . 101 4.3.1 The Environment . 102 4.3.2 The Chemokine Space . 102 4.3.3 Agents and Their Rules . 104 4.3.4 The Simulation . 105 4.4 Simulation Platform . 106 4.5 Results Model . 109 4.5.1 An Example Run . 110 4.5.2 Early and Later Stages of Infections . 111 4.5.3 Varying T-cells Arrival Time . 117 4.5.4 Intracellular Bacterial Growth Rate . 121 4.6 Validation . 121 4.7 Conclusion . 126 5 Granuloma Formation Algorithm 127 5.1 Design Principles for Distributed Decision Making . 128 5.1.1 Design Principle I . 129 5.1.2 Design Principle II . 129 5.1.3 Design Principle III . 130 5.1.4 Design Principle IV . 130 3 5.2 The Algorithm . 131 5.2.1 Mapping the Design Principles to the Algorithm . 132 5.3 Conclusions . 138 6 Experimental Methods and Results Analysis 140 6.1 Experimental Protocol . 140 6.1.1 Simulation Platform . 143 6.1.2 Performance Metrics . 144 6.1.3 Significance Test . 144 6.2 Experiment I: !-algorithm . 144 6.2.1 Description . 145 6.2.2 Results and Analysis . 145 6.3 Experiment II: Single Nearest Charger Algorithm . 149 6.3.1 Description . 149 6.3.2 Results and Analysis . 151 6.4 Experiment III: Shared Nearest Charger Algorithm . 156 6.4.1 Description . 157 6.4.2 Results and Analysis . 159 6.4.3 Experimental Findings . 166 6.5 Experiment V: Granuloma Formation Algorithm . 166 6.5.1 Description . 167 6.5.2 Results and Analysis . 167 6.6 Conclusions . 172 7 Reflections on the Development of Immune-inspired Solution for Swarm Robotic Systems 177 7.1 Summary of Work . 178 7.1.1 Understanding the Problem Domain in Swarm Robotic Systems . 178 7.1.2 Applying Conceptual Framework in Developing Immune-Inspired Solution for Swarm Robotic Systems . 180 7.2 Reflections on the Development of Immune-inspired Solution for Swarm Robotic Systems . 184 7.2.1 Reflections on the Conceptual Framework . 184 7.2.2 Reflections on Swarm Robotic Systems . 187 7.3 Conclusions and Future Work . 189 A Robustness Analysis 192 A.1 Description . 192 4 A.2 Results and Analysis . 193 A.3 Conclusion . 197 Glossary 200 5 List of Tables 2.1 Classification of the role of Swarm Intelligence and Artificial Immune Systems in Science and Engineering (Timmis et al., 2010a) . 34 3.1 Properties of swarm robotics and granuloma formation . 85 4.1 Cells and their functions in granuloma formation . 100 4.2 Parameter for the Netlogo simulator for the agent-based model of granu- loma formation. 108 4.3 Mapping from the domain model to Netlogo. 109 4.4 Initial parameter definitions of the simulation. 111 4.5 Parameter definitions of the simulation. 121 4.6 Agents in granuloma formation and swarm robots . 124 5.1 Mapping from granuloma formation signalling mechanism (in figure 4.10) to granuloma formation algorithm signalling and interactions (in figure 5.2).133 6.1 Robot fixed parameters for all simulations according to experiments con- ducted in Bjerknes (2009) with epuck robots. 142 6.2 Results for difference in time between the robot’s wheel and robot’s led when the robot stops moving . 142 6.3 Variable parameters for failing scenario in the environment . 142 6.4 P and A values for mean centroid distance on robots between M1 and M2 in 1100 simulation seconds. 154 6.5 P and A values for mean centroid distance on robots between M3 and M2 in 1100 simulation seconds. 161 6 6.6 P and A value for mean centroid distance on robots between M3 and M1 in 1100 simulation seconds. 163 6.7 P and A value for mean centroid distance on robots between M4 and M3 in 1000 simulation seconds. 171 6.8 P and A value for mean centroid distance on robots between M4 and M2 in 1000 simulation seconds. 171 7.1 Properties of swarm robotics and granuloma formation . 182 A.1 The magnitude of effect size indicated by A test score for different sim- ulation runs for single nearest charger algorithm with one, two, three, four and five failing robots. A test scores with less than 0.56 (small) are marked with ∗ which are seen in the case of one and two failing robots. 193 A.2 The magnitude of effect size indicated by A test score for different sim- ulation runs for shared nearest charger algorithm. A test score with less than 0.56 (small) is marked with ∗ which are seen in the case of one and two failing robots. 195 A.3 The magnitude of effect size indicated by A test score for different simu- lation runs for granuloma formation algorithm. A test score with less than 0.56 (small) are marked with ∗ ....................... 197 7 List of Figures 2.1 The setup of swarm beacon taxis. A swarm of robots (left) with limited sensors must move to a beacon (on the right). 43 2.2 The illuminated (light-coloured circle).
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