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Publication Type : Conference Proceedings
Source : In Advances in Robotics - 5th International Conference of The Robotics Society, Indian Institute of Technology, Kanpur, Article No.: 23, pp. 1-6, June, 2021
Url : https://dl.acm.org/doi/abs/10.1145/3478586.3478588
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Verified : No
Year : 2021
Abstract : 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.
Cite this Research Publication : Sivasankar Ganesan, Senthil Kumar Natarajan, and Asokan Thondiyath, “G-RRT*: Goal-oriented sampling-based RRT* path planning Algorithm for mobile robot navigation with improved convergence rate.” In Advances in Robotics - 5th International Conference of The Robotics Society, Indian Institute of Technology, Kanpur, Article No.: 23, pp. 1-6, June, 2021.