Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments
| Autor: | Flaspohler, Genevieve, Preston, Victoria, Michel, Anna P. M., Girdhar, Yogesh, Roy, Nicholas |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IEEE Robotics and Automation Letters (RA-L) 2019 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/LRA.2019.2929997 |
| Popis: | We present PLUMES, a planner to localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. The "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 continous-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. Comment: 8 pages, 8 figures, To appear in the proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019 Macau |
| Databáze: | arXiv |
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