LAUSR

Path planning is a major challenging problem for mobile robots, as the robot is required to reach the target position from the starting position while simultaneously avoiding conflicts with obstacles. This paper refers to a novel method as short and safe Q-learning to alleviate the short and safe path planning task of mobile robots. To solve the slow convergence of Q-learning, the artificial potential field is utilized to avoid random exploration and provides a priori knowledge of the environment for mobile robots. Furthermore, to speed up the convergence of the Q-learning and reduce the computing time, a dynamic reward is proposed to facilitate the mobile robot towards the target point. The experiments are divided into two parts: short and safe path planning. The mobile robot can reach the target with the optimal path length in short path planning, and away from obstacles in safe path planning. Experiments compared with the state-of-the-art algorithm demonstrate the effectiveness and practicality of the proposed approach. Concluded, the path length, computing time and turning angle of SSQL is increased by 2.83%, 23.98% and 7.98% in short path planning, 3.64%, 23.42% and 12.61% in safe path planning compared with classical Q-learning. Furthermore, the SSQL outperforms other optimization algorithms with shorter path length and smaller turning angles.