A SRT-based path planning algorithm in unknown complex environment

Aiming to the robot path planning problem in the unknown complex environment, this paper presents an improved Sensor-based Random Tree (SRT) path planning algorithm. First, RRT and SRT path planning algorithms are briefly introduced. Then, an original algorithm which uses the real-time local information from robot sensors and the target tendency method to make the online planning is presented. The algorithm overcomes SRT's redundant branches and low efficiency by combining the nonholonomic constraint conditions and the candidate viewpoint optimization, on which improves SRT's planning efficiency, and guarantees its stability as well. Finally, simulated experiments are done on Player/Stage and MRDS platforms, and the results verified the reliability and effectiveness of the proposed algorithm.