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Sean Wilson

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2 papers

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ICRA Conference 2021 Conference Paper

Range Limited Coverage Control using Air-Ground Multi-Robot Teams

Max Rudolph
Sean Wilson
Magnus Egerstedt

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an a priori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd’s algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to Lloyd’s algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd’s algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.

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ICRA Conference 2020 Conference Paper

Multi-Agent Task Allocation using Cross-Entropy Temporal Logic Optimization

Christopher Banks
Sean Wilson
Samuel Coogan 0001
Magnus Egerstedt

In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (LTL) specifications and consider agent specific cost functions. We propose to use the stochastic optimization technique, cross entropy, to optimize over this cost function. The multi-agent task allocation cross-entropy (MTAC-E) algorithm is developed to determine both when it is optimal to switch to a new agent to complete a task and minimize the costs associated with individual agent trajectories. The proposed algorithm is verified in simulation and experimental results are included.

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