Abstract
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).
Highlights
The ocean is a complex dynamical system
The novelty of rapidly-exploring adaptive sampling tree (RAST)* includes: (1) utilizing the tournament selection method in replacement of random sample, so that more new branches will fall in higher scientific interest areas, which can result in finding optimal solutions quickly and saving computation time; (2) modifying the heuristic procedure from distance to information gathering per hour in order to grow branches that can gain more information with less traveling time
RAST*, RRST*, and RAST are inspired by rapidly-exploring random tree (RRT), the additional steps of intelligent sampling procedure that have been introduced in these three algorithms have complexities that are insignificant enough to have an effect on the complexity of the algorithm
Summary
The ocean is a complex dynamical system. To understand ocean dynamics and increase accuracy of ocean models, measurements need to be taken with high spatio-temporal resolution according to the ocean phenomena under investigation [1,2,3,4]. This research presents a variant of RRT*, referred to as rapidly-exploring adaptive sampling tree star (RAST*), that combines inspirations from RRT* with the tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. The novelty of RAST* includes: (1) utilizing the tournament selection method in replacement of random sample, so that more new branches will fall in higher scientific interest areas, which can result in finding optimal solutions quickly and saving computation time; (2) modifying the heuristic procedure from distance to information gathering per hour in order to grow branches that can gain more information with less traveling time.
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