Artificial potential field-based anti-saturation positioning obstacle avoidance control for wheeled robots

Abstract

This paper studies the anti-saturation positioning and obstacle avoidance control of wheeled robots. First, an improved direction error auxiliary function (DEAF) is proposed to ensure that the robot speed is reduced when the orientation error is significant. Compared with previous work, the smoothness of the DEAF is extended to the $$n$$ -th order. Meanwhile, the hyperbolic tangent function is introduced to ensure the boundedness of the control law. Then, a novel Gaussian barrier Lyapunov function (BLF) is designed by introducing a smooth switching function. Compared with the traditional Gaussian function, the range of the repulsion field can be adjusted by parameters. Finally, the control law for avoiding multiple obstacles is obtained by combining the novel Gaussian BLF and the improved DEAF. Different from the previous multiple obstacles avoidance methods, the proposed approach plans the range of the repulsion field according to the distance between the obstacles instead of planning the robot path. The simulation results show the effectiveness of the proposed control algorithms.