快速探索自适应采样树*：一种基于样本的路径规划算法，用于在可变海洋环境中进行无人海洋车辆信息收集。

Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments.

机构信息

School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China.

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

出版信息

Sensors (Basel). 2020 Apr 29;20(9):2515. doi: 10.3390/s20092515.

DOI:10.3390/s20092515

PMID:32365553

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7249061/

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).

摘要

本研究提出了一种新的基于样本的路径规划算法，用于自适应采样。目标是为无人海洋车辆 (UMV) 找到一条近最优路径，在满足避碰约束和预定任务时间的前提下，最大限度地收集科学兴趣区域内的信息。所提出的快速探索自适应采样树星 (RAST*) 算法结合了快速探索随机树星 (RRT*) 的灵感，采用锦标赛选择方法和信息启发式方法，以实现连续空间中信息数据的有效搜索。数值实验和概念验证现场实验的结果表明，所提出的 RAST* 算法优于快速探索随机采样树星 (RRST*)、快速探索自适应采样树 (RAST) 和粒子群优化 (PSO) 算法。