Combining Geometric and Information-Theoretic Approaches for Multi-Robot Exploration

• URL: http://arxiv.org/abs/2004.06856v1
• Date: Wed, 15 Apr 2020 02:02:12 GMT
• Title: Combining Geometric and Information-Theoretic Approaches for Multi-Robot Exploration
• Authors: Aravind Preshant Premkumar, Kevin Yu, and Pratap Tokekar
• Abstract summary: We show that the exploration time of our algorithm is competitive (as a function of $p$) with respect to the offline optimal exploration algorithm. The algorithm is based on a single-robot polygon exploration algorithm, a tree exploration algorithm for higher level planning and a submodular orienteering algorithm for lower level planning.
• Score: 16.010307336422148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an algorithm to explore an orthogonal polygon using a team of $p$ robots. This algorithm combines ideas from information-theoretic exploration algorithms and computational geometry based exploration algorithms. We show that the exploration time of our algorithm is competitive (as a function of $p$) with respect to the offline optimal exploration algorithm. The algorithm is based on a single-robot polygon exploration algorithm, a tree exploration algorithm for higher level planning and a submodular orienteering algorithm for lower level planning. We discuss how this strategy can be adapted to real-world settings to deal with noisy sensors. In addition to theoretical analysis, we investigate the performance of our algorithm through simulations for multiple robots and experiments with a single robot.

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