运动规划
随机树
趋同(经济学)
计算机科学
路径(计算)
采样(信号处理)
移动机器人
算法
数学优化
快速行进算法
任意角度路径规划
机器人
数学
人工智能
计算机视觉
滤波器(信号处理)
经济
程序设计语言
经济增长
作者
Sivasankar Ganesan,Senthil Kumar Natarajan,Asokan Thondiyath
标识
DOI:10.1145/3478586.3478588
摘要
Randomized sampling-based path planning algorithms are widely used for mobile robot navigation in complex configuration space. The optimal Rapidly Exploring Random Tree (RRT*) is one of the popular sampling-based path planning algorithms that guarantees collision-free optimal path planning solutions. Even though the RRT* path planning algorithm is asymptotically optimal, its convergence is very slow. To address this problem, this paper proposes a Goal-oriented RRT* algorithm called G-RRT*. The key idea of G-RRT* is to reduce the sampling space by generating more samples near the goal configuration. The proposed algorithm is validated in a maze environment using existing algorithms. The proposed G-RRT* path planning algorithm outperforms RRT* and Informed RRT* in three performance measures convergence time, the initial cost solution, and the number of nodes visited.
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