Bi-Directional Adaptive Probabilistic Method With a Triangular Segmented Interpolation for Robot Path Planning in Complex Dynamic-Environments

摘要

Path planning is a fundamental aspect of mobile robots and autonomous systems. Methods of path planning are used in robotics to create a path for a robot or autonomous system to follow from a starting position to a goal one while avoiding obstacles and satisfying any additional conditions. There are many different methods to plan the path, including probabilistic methods, heuristics-based approaches, and optimization-based methods. In this paper, we propose a new path planning method called Dynamic Adaptive RRT-connect with a Triangular Segmented Interpolation. Our method improves the traditional RRT algorithms by using an Adaptive-RRT approach, where a random node is chosen as a new node to increase tree exploration. Then, we use a Bi-directional scheme to further enhance the convergence time and cost. Additionally, our method employs Triangular Segmented Interpolation (TSI) method to improve path length and smoothness. Finally, we operate this method within a dynamic environment depending on the Dynamic Window Approach (DWA). Experiments on a variety of environments have shown that our proposed method achieves better than the RRT and RRT-connect algorithms individually in terms of computation time (reduced by 90-80%), cost (reduced by 82-63%), and path length (shorten by 17-12%) besides the ability to avoid dynamic obstacles efficiently.