Journal
SENSORS
Volume 20, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s20092515
Keywords
path planning; unmanned marine vehicles; adaptive ocean sampling; rapidly-exploring adaptive sampling tree star
Funding
- National Natural Science Foundation of China [41706108, 41527901]
- Shanghai Sailing Program [17YF1409600]
- open project of Qingdao National Laboratory for Marine Science and Technology [QNLM2016ORP0104]
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This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).
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