Sampling-Based Optimal Motion Planning Algorithm with disjointed-Trees in Constrained Environment
Yi Zhang, Hongguang Wang · 2023
Sampling-based motion planning techniques have produced many efficient solutions in robotics research. Motion planning has expanded from straightforward use cases to challenging application settings, but it is challenging to perform in a constrained environment with obstacles. In this study, we suggest a rapid exploration random tree-based motion planning method that starts at various motion planning stages, from sampling to connection. By processing the sampling points close to the obstacles on the one hand, the suggested technique optimizes the spread of points in the workspace. On the other hand, it creates numerous free arms to increase workspace visibility. Together., the two elements increase the tree's development and the connectivity of its paths in challenging workspace locations. Numerous irregular obstacles and tight spaces are created for experiments, and the suggested motion planner is contrasted with several traditional motion planners. According to experimental data, the proposed method is more stable in providing optimal solutions and requires less sampling time in the limited passage scenario.