Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 33, Issue 9, Pages 1271-1287Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364914533443
Keywords
Motion and path planning; field robotics; robotic information gathering
Categories
Funding
- Office of Naval Research [N00014-09-1-0700, N00014-07-1-00738, N00014-06-10043]
- National Science Foundation [CCR-0120778, CNS-1035866, IIS-1317815]
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We propose three sampling-based motion planning algorithms for generating informative mobile robot trajectories. The goal is to find a trajectory that maximizes an information quality metric (e. g. variance reduction, information gain, or mutual information) and also falls within a pre-specified budget constraint (e. g. fuel, energy, or time). Prior algorithms have employed combinatorial optimization techniques to solve these problems, but existing techniques are typically restricted to discrete domains and often scale poorly in the size of the problem. Our proposed rapidly exploring information gathering (RIG) algorithms combine ideas from sampling-based motion planning with branch and bound techniques to achieve efficient information gathering in continuous space with motion constraints. We provide analysis of the asymptotic optimality of our algorithms, and we present several conservative pruning strategies for modular, submodular, and time-varying information objectives. We demonstrate that our proposed techniques find optimal solutions more quickly than existing combinatorial solvers, and we provide a proof-of-concept field implementation on an autonomous surface vehicle performing a wireless signal strength monitoring task in a lake.
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