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Swarm Robotics: From sources of inspiration to domains of application What is Swarm Robotics? • Yet another novel approach to the control of large group of robots! • Study of multi-robot coordination strategies inspired from social insects. • Engineering self-organization in physically embodied swarms. • Application of Swarm Intelligence to the control of a group of robots. 2/28 Attempting to define the term Swarm Robotics? Need to identify – aspects that make swarm robotics approach novel and desirable. – aspects that distinguishes swarm robotics from other related studies. 3/28 What’s novel and desirable in the Swarm Robotics approach? • Emphasis on the system-level functioning properties observed in social insect systems: – Robustness – Flexibility – Scalability • Essential for deploying large numbers of robots. 4/28 Robustness • Social insects can continue to operate despite large disturbances. – – – – Redundancy Decentralized coordination Simplicity of the individuals Distributed sensing 5/28 Flexibility • Social insects can offer modularized solutions to tasks of different nature by utilizing different coordination mechanisms. 6/28 Flexibility – same swarm, different tasks • Foraging • Prey retrieval • Chain formation 7/28 Scalability • Social insects are observed to be able to operate under a wide range of group sizes. That is, coordination mechanisms are rather independent of the number of individuals in the group. 8/28 Putting swarm robotics in place • Where is Swarm Robotics placed in relation to other related studies? • Aspects that distinguish swarm robotics studies from: – other flavors of multi-robot studies – other related studies such as Swarm Intelligence, Sensing networks, etc.. 9/28 0 - Individuals should be robots! • Individuals should be autonomous robots. – Individuals should • be situated and autonomous • be able to physically interact • Mobility of individuals is sufficient, but not required. • Metamorphic robotic systems? – Yes • Sensor networks? – No 10/28 1 - Large number of robots • The study should be relevant for the coordination of large numbers of robots. – Why relevancy? – How large is “large”? 11/28 2 - Few homogeneous groups of robots • The robotic system should consist of few homogeneous groups and that the number of robots in each group should be large. – Teams are not swarms. – Hierarchical robotic systems (for instance swarms with a “designated queen”) are less `swarm robotic’. – What’s a homogeneous group? – How about individual adaptation? 12/28 3 - Relatively incapable of inefficient robots • The robotic system should utilize relatively incapable or inefficient robots with respect to the task at hand. – The robots should have difficulties in carrying the task on their own. – The deployment of a group of robots should improve the performance of system. – The deployment of a group of robots should improve the robustness of the system. 13/28 4 - Robots with only local sensing and communication abilities • For coordinating their actions, the robots should utilize only local sensing and communication capabilities. – Locality promotes scalability. – Existence of global communication channels not used for coordination among the robots does not violate. 14/28 Criteria for Swarm Robotic systems • A swarm robotic system should consist of – – – – large numbers of robots, few homogeneous groups of robots, robots that are relatively incapable or inefficient, robots with only local sensing and communication abilities. • Not a checklist for evaluating a study. • But as yardsticks to evaluate how `swarm robotic’ a given study is. 15/28 Finally… a definition Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. 16/28 Sources of inspiration • Self-organizing natural systems – Social insect systems: ants, termites, wasps, bees, cockroaches, locusts… – Animals with social behaviors: penguins, birds, fish, sheep... – Unicellular organisms: Amoebae, bacteria, viruses… • Artificial self-organizing systems – Amorphous computing – Self-assembly of materials 17/28 Aggregation of amoebae into slime mould • When food is abundant, amoebae (D. discoideum) acts independently of others, feeding and multiplying (Bonner;1967, Goldbeter;1996). • When food supply is depleted amoebae release cAMP ( a chemo-attractant for amoeba) into the extra-cellular environment. • Amoebae aggregate forming a slug, a multi-cellular organism which can move and sporulate. Summarized from Self-Organization in Biological Systems by Scott Camazine, Jean-Louis Deneubourg, Nigel R. Franks, James Sneyd, Guy Theraulaz, and Eric Bonabeau 18/28 Aggregation mechanism • Amoebae secrete cAMP leading to spiral waves that streams the cells to the center – Positive feedback mechanisms • Release cAMP with a certain period (oscillatory mode). • If hit by a cAMP pulse, amplify it (relay mode). – Negative feedback mechanism • High cAMP concentrations briefly desensitize the receptors. • Amoebae moves in the direction of increasing cAMP concentration (at 1/10 of the cAMP wave speed). • Cell-to-cell adhesion makes amoeba clumps persistent. 19/28 Take-home lesson/inspiration • The mechanism discussed aggregate 10,000100,000 cells! • In a recent study (Dorigo et al.;2004), it is shown that aggregating tens of robots (equipped with simulated speakers and microphones) is very challenging. • Stigmergy seems to be a key element for scalability! • Stigmergy in a swarm of robots – Natural stigmergy: Using water, chemicals, etc.. – Stigmergy using embedded systems: e.g. Gnats (Balch;) – Stigmergy using robots: Use some of the robots as the “medium” while others aggregate. 20/28 Quorum sensing in bacteria • Bacteria seem to have interesting communication mechanisms to increase their survival. • V. fischeri produces light when its population reach a critical size. • V. cholarae delays the production of virulence factor until they reach a certain mass, to ensure a successful infection against the infection system. • Recent studies show that bacteria use certain auto-inducers to detect their density in the environment. B.L. Bassler, How bacteria talk to each other: regulation of gene expression by quorum sensing. Current Opinions in Microbiology 1999 Dec;2(6):582-7. 21/28 Take home lesson/inspiration • Quorum sensing will be an essential problem for swarm robotic systems. • The density of individuals is an important parameter in natural swarms which can lead to bifurcations in swarm behavior. • Density measurement w/o stigmergy is likely to be an interesting challenge. 22/28 Information exchange in bacteria • Bacterial colonies can be more resistant to antibiotics than bacteria living in suspension! • Hypothesis: Bacteria form a “genomic web” communicating with each other: – Inducive communication: a chemical signal triggers a certain action in other bacteria. – Informative communication: the message received is interpreted by the cell, and its response is determined by its history as well as its current state. E. Ben-Jacob, Bacterial self-organization: co-enhancement of complexification and adaptability in a dynamic environment. Phil. Trans. R. Soc. Lond. A, 361, pp 1283– 1312, 2003. 23/28 Take home lesson/inspiration • In real life, some individuals of a swarm robotic systems will probably discover certain hazards the hard way. • Individuals should be able to pass last-minute signals and information to the rest of the swarm. 24/28 Amorphous computing • Challenge: “How can prespecified, coherent behavior be engineered from the cooperation of vast numbers of unreliable parts interconnected in unknown and time-varying ways?” • Medium: “a system of irregularly placed, asynchronous, locally interacting computing elements”. • Inspiration and approach: morphogenetic processes in biological systems such as tissue growth. Amorphous Computing, Abelson et al, Communications of the ACM, Volume 43, Number 5, May 2001. 25/28 Take home lesson/inspiration • Amorphous computing nodes [if and when they become available] can be “active intelligent pheromones” of swarm robotic systems. • Swarm robotic systems, when immobile, are amorphous computing mediums and can utilize their programming paradigms. 26/28 Self-assembly • Self-assembly: self-organization by making physical bond formation – Individuals lose some of their motility. This creates some interesting dynamics. Social insects and breakable bonds in chemistry • Self-assembly of materials is described as the “autonomous organization of components into patterns or structures without [external] intervention.” Whitesides and Grzybowski (Science; 2002) • Self-assembly is a promising method for fabricating regular structures: nano-scale self-assembly is promising for building large numbers of micro- electro-mechanical systems (MEMS), improving the robotic assembly processes. 27/28 Take home lesson/inspiration • Use of templates for scaffolding the selfassembly/organization process to reduce defects in the structure. • Catalytic agents to improve the self-assembly process. 28/28