Path Planning Based On Rapidly-Exploration Random Tree (RRT)

Path planning from an initial position to the target point is one of the critical challenges in autonomous mobile robot applications over diverse environment scenarios. The selection of the best route for safe and robust navigation depends mainly on the environmental model, the type of mobile robot, and the considered application. In this paper, an artificial intelligent (AI) control technique is employed for efficient robot path planning with collision avoidance. In particular, the bidirectional rapidly-exploration random tree (BiRRT) algorithm is proposed to significantly reduce the travel path distance compared with the reference RRT benchmark. This has a direct impact on minimizing the processing time response and consumed power resources. Simulation results of different environment map scenarios demonstrated the effectiveness of the BiRRT approach for mobile robotic applications.