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

Abstract

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

“…Ref. [7] described a method of learning in which the robot builds a model of the world and a network of nearby areas. Finding the most viable route may be done in a variety of ways, and many of these techniques vary in the way that paths and nodes are specified.…”

“…When constructing a connectivity graph, each cell or its border line is used to create each node [2]. Each node in the graph indicates a cell that is directly adjacent to another cell [7].…”

“…Many researchers have used this method because it is elegant, safe, and simple. The robot is affected by the fields created by the beginning, destination, and barrier locations in the solution space and the surrounding area [7]. The target point attracts attention, whereas the default position and obstacles detract attention.…”

“…The robot must be able to figure out the best way to get there in the shortest amount of time, distance, and money. It is possible to divide path planning into global and local planning, depending on the scope of the map [7]. Global path planning provides all the necessary data about the robot's known environment, while local path planning relies on partially or completely zero knowledge of the robot's environment to plan its course.…”

A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms

“…Some algorithms present difficulties working in complex environments with narrow corridors, a high number of obstacles, or a high number of DoFs. Other path-planning algorithms need a post-processing stage to smooth out the obtained path for implementation in a real robot [ 16 , 23 , 29 , 30 ].…”