Path Planning Optimization for Obstacle Avoidance in Unknown Environment
Hao Liu, Xi Chen, Chang Li · 2024
In this paper, the path planning for a robot in an unknown environment is considered. To enable the robot to reach the target point while ensuring obstacle avoidance, a novel framework based on the Artificial Potential Field (APF) method is proposed. The APF consists of two components: an attractive potential field generated by the target point and a repulsive potential field created by the perceived obstacle feature points. The robot is guided under the APF to optimize its path towards reducing the potential value. Furthermore, three robotic modes are proposed to avoid the robot getting trapped in local APF minimum. Each mode is equipped with corresponding potential fields and control laws, enabling the robot to complete the task along a shorter path by appropriately switching its mode. At last, the effectiveness of our proposed method is illustrated via a numerical example.