论文标题
杂种空中水下车辆的信息驱动的路径计划
Information-driven Path Planning for Hybrid Aerial Underwater Vehicles
论文作者
论文摘要
本文介绍了一种新型的快速探索自适应采样树（Rast）算法，用于在空气3D环境中混合空中水下汽车（HAUV）的自适应采样任务。该算法创新地结合了基于比赛的点选择采样策略，启发式搜索过程和快速探索随机树（RRT）算法的框架。因此，可以将车辆引导到科学家的感兴趣区域进行采样，并生成一条无冲突的路径，以最大程度地收集HAUV的信息，这是在电流或风能的环境效应限制下，预算有限的预算。模拟结果表明，与快速探索的信息收集树（RIGT）算法和粒子群优化（PSO）算法相比，快速搜索自适应采样树算法具有更高的优化性能，更快的解决方案速度和更好的稳定性。
This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST) algorithm for the adaptive sampling mission of a hybrid aerial underwater vehicle (HAUV) in an air-sea 3D environment. This algorithm innovatively combines the tournament-based point selection sampling strategy, the information heuristic search process and the framework of Rapidly-exploring Random Tree (RRT) algorithm. Hence can guide the vehicle to the region of interest to scientists for sampling and generate a collision-free path for maximizing information collection by the HAUV under the constraints of environmental effects of currents or wind and limited budget. The simulation results show that the fast search adaptive sampling tree algorithm has higher optimization performance, faster solution speed and better stability than the Rapidly-exploring Information Gathering Tree (RIGT) algorithm and the particle swarm optimization (PSO) algorithm.
