Improved Informed-RRT Based Path Planning and Trajectory Optimization for Mobile Robots

• Published in: International journal of precision engineering and manufacturing Vol. 24; no. 3; pp. 435 - 446
• Main Authors: Yuan, Laohu, Zhao, Jiance, Li, Wenhao, Hou, Jian
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.03.2023; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Barriers; Blindness; Engineering; Industrial and Production Engineering; Materials Science; Motion control; Nodes; Regular Paper; Sampling; Strategy; Trajectory optimization; Trajectory planning; Minimum snap; Path planning; Informed-RRT; Trajectory optimization
• ISSN: 2234-7593; 2005-4602
• DOI: 10.1007/s12541-022-00756-6

In order to solve the problems of the Informed-RRT* algorithm in path planning, such as blindness, uneven sampling, and unsmooth paths, an improved Informed-RRT* algorithm based on adaptive growth strategy and elliptical region variable weight sampling strategy with trajectory optimization is proposed in this paper. At first, an adaptive growth strategy is developed to address the blindness issue by considering the effect of the target point and any obstacles when selecting new nodes. Second, to address the issue of unequal sampling within the ellipse, an elliptical region variable weight sampling strategy is presented. This strategy increases the number of nodes in the vicinity of the target point. Finally, the minimum snap method is used to determine the best trajectory for the non-smooth path. The dynamic corridor constraint is used to reduce the difference between the trajectory and the path as small as possible while avoiding collisions with obstacles. The simulation experiments show that the improved algorithm cuts by 27.83% the time it takes to find the initial path, and by 16.91% the time it takes to find the best path. The optimized and smoothed path can be turned into acceleration at the right time to make motion control easier.