Optimization of Stochastic Strategies for Spatially Inhomogeneous Robot Swarms: A Case Study in Commercial Pollination
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CitationBerman, Spring, Radhika Nagpal, and Ádám Halász. 2011. Optimization of stochastic strategies for spatially inhomogeneous robot swarms: a case study in commercial pollination. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'11): September 25-30, San Francisco, CA, 3923-3930. Los Alamitos, Calif: IEEE Computer Society Press.
AbstractWe present a scalable approach to optimizing robot control policies for a target collective behavior in a spatially inhomogeneous robotic swarm. The approach can incorporate robot feedback to maintain system performance in an unknown environmental ﬂow ﬁeld. We consider systems in which the robots follow both deterministic and random motion and transition stochastically between tasks. Our methodology is based on an abstraction of the swarm to a macroscopic continuous model, whose dimensionality is independent of the population size, that describes the expected time evolution of swarm subpopulations over a discretization of the environment. We incorporate this model into a stochastic optimization method and map the optimized model parameters onto the robot motion and task transition control policies to achieve a desired global objective. We illustrate our methodology with a scenario in which the behaviors of a swarm of robotic bees are optimized for both uniform and nonuniform pollination of a blueberry ﬁeld, including in the presence of an unknown wind.
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