Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL)

Path planning is an important task for mobile service robots. Most of the available path-planning algorithms are applicable only in static environments. Achieving path planning becomes a difficult task in an unknown dynamic environment. To solve the problem of path planning in an unknown dynamic environment, this paper proposes a BRRT*- DWA algorithm with Adaptive Monte Carlo Localization. Bidirectional Rapidly-exploring Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used as a localization technique. By using the map file of the unknown environment created by SLAM and LiDAR sensor, the robot can navigate while avoiding dynamic as well as static obstacles. In addition, the object identification algorithm YOLO was adopted, trained, and used for the robot to recognize objects and people. Results obtained from both simulation and experiment show the proposed method can achieve better performance in a dynamic environment compared with other state-of-the-art algorithms.