Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
An Behavior-based Robotics
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Data collection, storage, and retrieval with an underwater sensor network
Proceedings of the 3rd international conference on Embedded networked sensor systems
Towards swarm calculus: universal properties of swarm performance and collective decisions
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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We propose a combined spatial and non-spatial probabilistic modeling methodology motivated by an inspection task performed by a group of miniature robots. Our models explicitly consider spatiality and yield accurate predictions on system performance. An agentâ聙聶s spatial distribution over time is modeled by the Fokkerâ聙聰Planck diffusion model and complements current non-spatial microscopic and macroscopic models that model the discrete state distribution of a distributed robotic system. We validate our models on a microscopic level based on sub-microscopic, embodied robot simulations as well as real robot experiments. Subsequently, using the validated microscopic models as our template, abstraction is raised to the level of macroscopic difference equations. We discuss the dependency of the modeling performance on the distance from the robot origin (drop-off location) and temporal convergence of the team distribution. Also, using an asymmetric setup, we show the necessity of spatial modeling methodologies for environments where the robotic platform underlies drift phenomena.