Sampling-based robotic information gathering algorithms Public Deposited

http://ir.library.oregonstate.edu/concern/articles/37720f628

This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the Author(s) and published by SAGE Publications. It can be found at:  http://ijr.sagepub.com/

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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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  • Hollinger, G. A., & Sukhatme, G. S. (2014). Sampling-based robotic information gathering algorithms. The International Journal of Robotics Research, 33(9), 1271-1287. doi:10.1177/0278364914533443
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  • description.provenance : Submitted by Deanne Bruner (deanne.bruner@oregonstate.edu) on 2015-02-18T00:24:23Z No. of bitstreams: 1 HollingerGeoffreyMechIndMfgEngnSamplingBasedRoboticInformation.pdf: 1932253 bytes, checksum: 2f5465d17ba0b5041e840772ecfb6f7b (MD5)
  • description.provenance : Made available in DSpace on 2015-02-18T00:25:22Z (GMT). No. of bitstreams: 1 HollingerGeoffreyMechIndMfgEngnSamplingBasedRoboticInformation.pdf: 1932253 bytes, checksum: 2f5465d17ba0b5041e840772ecfb6f7b (MD5) Previous issue date: 2014-08
  • description.provenance : Approved for entry into archive by Deanne Bruner(deanne.bruner@oregonstate.edu) on 2015-02-18T00:25:22Z (GMT) No. of bitstreams: 1 HollingerGeoffreyMechIndMfgEngnSamplingBasedRoboticInformation.pdf: 1932253 bytes, checksum: 2f5465d17ba0b5041e840772ecfb6f7b (MD5)

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