Dual-mechanism tangential obstacle avoidance of autonomous robots in dynamic environment
Aiming at the dynamic randomness of robot working environment,an improved artificial potential field method based on dual-mechanism tangential obstacle avoidance was proposed.Aiming at the local minimum trap of the traditional artificial potential field method,a static obstacle avoidance mechanism was proposed.The map was preprocessed before planning,local minimum points were predicted and obstacles were divided into connected and non-connected,and the static tangential obstacle avoidance was realized by combining the tangential obstacle avoidance.Based on the static obstacle avoidance mechanism,a dynamic obstacle avoidance mechanism was proposed for dynamic obstacles.By adjusting the collision risk coefficient in real time and selecting the obstacle with the largest coefficient for obstacle avoidance angle compensation,the dynamic tangential obstacle avoidance was realized.By state decision making,the static and dynamic tangential obstacle avoidance mechanism and the global path planning were integrated to realize the hybrid planning and design.Simulation and omnidirectional mobile platform was designed,and the proposed method was verified and tested.Results showed that the proposed method effectively resolved the local minimum trap of the traditional artificial potential field method under different complex environments,and realized fast autonomous obstacle avoidance under dynamic environments.Comparing the average obstacle avoidance time of three methods to avoid different types of obstacles,the proposed method was 55%better than the dynamic window approach(DWA)and 40%better than the time elastic band(TEB).Comparing the average navigation time of three methods for navigating maps of different complexity,the proposed method was 39%better than DWA and 22%better than TEB.
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