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• W3114629335 abstract "In this work, we propose a method to solve the interaction planning problem for a set of mobile agents with obstacles and agent collisions via a core path planner and constrained random position sampling approach. The interaction constraint is posed in the form of an arbitrary number of discretized times in which we enforce a desired topological condition. The general objective function, to be maximized subject to the interaction constraint, is coverage of an environmental process here modeled as a Gaussian mixture model. The main tool we use to select positions and the times of interaction is the greedy algorithm, along with a submodular objective function and matroid constraint. Through this we guarantee strong theoretical lower bounds on sub-optimality. Simulations, including several Monte Carlo trials, are presented to corroborate our proposed methods." @default.
• W3114629335 created "2021-01-05" @default.
• W3114629335 creator A5031709486 @default.
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• W3114629335 date "2021-04-01" @default.
• W3114629335 modified "2023-09-25" @default.
• W3114629335 title "Multi-Agent Intermittent Interaction Planning via Sequential Greedy Selections Over Position Samples" @default.
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• W3114629335 doi "https://doi.org/10.1109/lra.2020.3047788" @default.
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