Post Triangular Rewiring Method for Shorter RRT Robot Path Planning

Abstract

This paper proposed the "post triangular rewiring" method that minimizes the sacrifice of planning time and overcomes the limit of optimality of sampling-based algorithms such as the rapidly exploring random tree (RRT) algorithm. The proposed "post triangular rewiring" method creates a path closer to the optimal path than the RRT algorithm before application, using the triangular inequality principle. Experiments were conducted to verify the performance of the proposed method. When the proposed method is applied to the RRT algorithm, the optimality efficiency increases compared to the planning time.

Recent advances in Rapidly-exploring random tree: A review

“…From the theoretical level, the feasible solutions can be further optimized in terms of evaluation metrics such as path length, complexity, and consumption time, etc. Some of the previous reviews also falls into post-processing, like Some kinds of RRT-Connect [ 35 , 36 , 45 , 46 ], informed-RRT* [ 23 , 92 , 96 ], Quick-RRT* [ 26 , 110 , 112 , 113 ], RRT*-Smart [ 73 , 79 , 80 ], Fast-RRT* [ 116 , 119 ], F-RRT* [ 7 , 118 ], post triangular rewiring strategy [ 52 , 137 ], interpolation post-processing [ 138 , 139 ]. From the application level, the feasible solutions are likely to not satisfy the kinematic model constraints of the robot or the scenario constraints in some application scenarios, therefore, the theoretically generated feasible solutions cannot be applied to real scenarios and further optimization is still required.…”

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

“…The RRT-like algorithms are being applied in various ways. For example, there is a method that generates an optimal path by applying a triangular inequality [5] and a method applicable to kinodynamic planning [6]. The RRT algorithm can be summarized as a method of planning a path by repeatedly inserting a randomly sampled location as a child node in a tree with the starting point as the root node until reaching the goal point.…”