Fig 1.
Diagram illustrating the obstacle avoidance principle of the APF method.
represents the influence range of the obstacle, Fatt represents the attractive force exerted by the target point on the robot, Frep represents the repulsive force exerted by the obstacle on the robot, and Fres represents the resultant force acting on the robot.
Fig 2.
The attraction force generated by the attractor set on the robot.
Fig 3.
Analysis of the repulsive force generated by a static obstacle.
Fig 4.
Analysis of the tangential force of the obstacle.
Fig 5.
Schematic diagram of the number of virtual target points occupied by the obstacle.
Fig 6.
The update step size of the attractor set in different regions.
Fig 7.
The deviation caused to the robot by dynamic obstacles.
Fig 8.
Schematic diagram of velocity and acceleration associated forces of dynamic obstacles.
Fig 9.
The schematic diagram of the region O1.
Fig 10.
The schematic diagram of the definitions and relationships of different regions.
Table 1.
Table of values for p.
Fig 11.
Explanation of tracking error calculation between trajectories.
Fig 12.
Static obstacle avoidance scene setup.
Fig 13.
Static obstacle avoidance trajectory analysis (for specific trajectory point data, please refer to the attachment ‘S1 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Table 2.
Tracking error comparison of obstacle avoidance trajectories in scenarios with static obstacles.
Fig 14.
Obstacle uniform linear motion scenarios.
Fig 15.
Obstacle uniform linear motion collision situation.
Fig 16.
Obstacle avoidance trajectories and corresponding tracking errors for obstacle uniform linear motion scenarios (for specific trajectory point data, please refer to the attachment ‘S2 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Table 3.
Tracking error comparison of obstacle avoidance trajectories in scenarios with obstacles moving at a constant linear speed.
Fig 17.
Experimental setup for complex motion scenarios with obstacles.
Fig 18.
Obstacle avoidance trajectory generation process based on the method in this paper (for specific trajectory point data, please refer to the attachment ‘S3 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Fig 19.
Obstacle avoidance trajectory generation process based on iAPF (for specific trajectory point data, please refer to the attachment ‘S4 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Fig 20.
Obstacle avoidance trajectory generation process based on DMP (for specific trajectory point data, please refer to the attachment ‘S5 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Fig 21.
Obstacle avoidance trajectories and corresponding tracking errors in complex motion scenarios with obstacles (for specific trajectory point data, please refer to the attachment ‘S6 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Table 4.
Tracking error comparison of obstacle avoidance trajectories in scenarios with obstacles exhibiting complex motion.
Fig 22.
Experimental setup under ablation experiments.
Table 5.
Design of different programs under ablation experiments.
Fig 23.
Obstacle avoidance trajectories and corresponding tracking errors in obstacle deceleration motion scenarios (for specific trajectory point data, please refer to the attachment ‘S7 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Table 6.
Tracking error comparison of obstacle avoidance trajectories in scenarios with obstacles exhibiting complex motion.
Fig 24.
Obstacle avoidance trajectories and corresponding tracking errors in obstacle accelerated motion scenarios (for specific trajectory point data, please refer to the attachment ‘S7 Table.xlsx’. For detailed descriptions of the data, please see ‘S1 Text.docx’).
Table 7.
Tracking error comparison of obstacle avoidance trajectories in scenarios with obstacles exhibiting complex motion.