Swarming Systems: Analysis (SSAM) Session 1
Time and Date: 14:15 - 15:45 on 19th Sep 2016
Room: Z - Zij foyer
Chair: Roland Bouffanais
|7000|| Less is more? New approaches for swarm control and inference
Abstract: Robot swarms are often said to exhibit emergent properties. Yet, it is possible to design controllers with predictable outcome. We illustrate this for two canonical problems, multi-robot rendezvous and cooperative transport. The simplicity of the controllers (some do not even require arithmetic computation) facilitates their analysis. In the second part of the talk, we address the problem of inferring the rules of swarming agents through observation. We propose Turing Learning - the first system identification method not to rely on pre-defined metrics - and test it on a physical swarm of robots. Finally, we discuss novel development tools. We present OpenSwarm, an operating system for miniature robots, and formal methods for automatic code generation. We report on experiments with up to 600 physical robots.
|Roderich Gross (The University of Sheffield)|
|7001|| Analysis and Design of Self-Organizing Heterogeneous Swarm Systems
Abstract: We present an overview of our recent work on self-organizing heterogeneous swarm systems that can show a wide variety of robust self-organizing spatio-temporal patterns. Our swarms consist of multiple types of very simple, kinetically interacting particles with no elaborate sensing, computation, or communication capabilities. We examine the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of swarm systems, which are characterized using several kinetic and topological metrics. Results showed that (a) heterogeneity of components had a strong impact on the structure and behavior of the swarms, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the swarms maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the production of more diverse behaviors of swarms, and (d) stochastic re-differentiation of components also naturally realized a self-repair capability of self-organizing patterns. We also explore evolutionary methods to design novel, non-trivial self-organizing patterns, using either interactive evolutionary computation or spontaneous evolution within an artificial ecosystem. Finally, we demonstrate that these self-organizing swarm systems are remarkably robust against dimensional changes from 2D to 3D, although spontaneous evolution works more efficiently in a 2D space.
|Hiroki Sayama (Binghamton University, State University of New York)|
|7002|| Failure is the nominal operation mode for swarms (of drones): reasons and consequences
Abstract: Swarms of drones (but the discussion applies to any kind of swarm) are a promising paradigm because combining individuals offers much more features than increasing the capacity of a single entity (?The whole is more than the sum of its parts? Aristotle, Metaphysics). It also raises a number of issues among which failure. In most systems, failure is considered an exception. But when thousands of autonomous entities communicating with each other and adapting their behavior so as to achieve a global mission (this is referred to as swarm intelligence) are considered, the situation is quite different. Statistically, a number of individuals will fail and a number of messages will be lost (because of collisions, interferences, e tc.). Thus, ?In adaptive systems [...] classical separation between ?nominal operation" and "faults" becomes untenable; system is continuously operating under faults? [ Werner J.A. Dahm, Director, Security & Defense Systems Initiative, Arizona State University in his keynote at AIAA Guidance, Navigation, and Control Conference 19 - 22 August 2013, Boston , Massachusetts ]. Applications should then be built so that the failure of an individual entity does not imply the failure of the global mission. In other words, an entity should not rely on any expected behavior of the other entities of the swarm. As a consequence, a mission should be designed respecting the following principles: a global result (a global property to attain) should be targeted instead of a local individual result, which could not be guaranteed; it should be qualitative rather than quantitative, since the worst case is always possible; no individual can assume a peer in the swarm is present in its neighborhood; no individual can assume a peer in the swarm is lost (it can simply be temporarily unreachable); no communication can be assumed to get through. A mission is thus de facto designed as an emergent behavior/property at the global swarm level that results from local individual behaviors. Obeying the above principles makes it possible to achieve real world missions that can be ?guaranteed? resilient to individual failures and communications faults. The counterpart (but it is worth the cost) is that it often leads to bigger resource consumption.
|Serge Chaumette (Bordeaux Computer Science Research Laboratory (LaBRI), University of Bordeaux)|
|7003|| Excess of Social Behavior Reduces the Capacity to Respond to Perturbations
Abstract: Social interaction increases significantly the performance of a wide range of cooperative systems, but natural swarms seem to limit the number of social connections. Flocking starlings interact on average with a fixed number of conspecifics and swarms of midges regulate their nearest-neighbor distance depending on the size of the swarm. This suggests that excessive social activity may have detrimental consequences. Using a canonical model of collective motion, we find that the responsiveness of a swarm is reduced when the social interaction exceeds a certain threshold. We find that the system can exhibit a large susceptibility even in the ordered phase (far from the critical point) if the amount of social interaction is set to an appropriate level. The same effect can be observed in collective decision-making models of distributed consensus, for example in a set of networked agents following the "majority vote" rule. If an external factor perturbs the state of a small sub-set of agents, this change will propagate through the network at a speed that depends on the number of social connections. These examples of distributed consensus show that an excess of social behavior can hinder their capacity to respond to fast perturbations. The result has far-reaching implications for the design of artificial swarms or interaction networks: even ignoring the costs of establishing connections and transmitting information between agents, it may be desirable to limit the number of connections in order to achieve a more effective dynamical response.
|David Mateo, Roland Bouffanais (Singapore University of Technology and Design)|