期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 4, 页码 3782-3789出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2929997
关键词
Motion and path planning; learning and adaptive systems; reactive and sensor-based planning; environmental monitoring and management
类别
资金
- National Science Foundation Graduate Research Fellowship Program award
- National Defense Science and Engineering Graduate Fellowship award
- National Science Foundation National Robotics Initiative Award [1734-400]
We present Plume Localization under Uncertainty using Maximum-ValuE information and Search (PLUMES), a planner for localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. This maximum seek-and-sample (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continuous-observation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.
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