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.