Path planning of factory handling robot integrating fuzzy logic-PID control technology

Abstract

Mobile robots have been widely used in various fields to assist people in completing various tasks. This study aims to enhance the efficiency of mobile robots in factory transportation tasks by improving the A-star algorithm and combining it with the dynamic window approach. Additionally, a fuzzy proportional-integral-differential (PID) controller is developed for adaptive path correction. To address the issue of robot driving deviation on complex roads, a PID controller is fused with fuzzy logic to adaptively adjust the implementation parameters and construct a path correction model. The test results show that the average time for A-star algorithm to search for a path is 5.19 s, the average number of grids searched is 160, and the average length of the search path is 30.2 cm. The average search path time of the improved A-star algorithm is 2.45 s, the average number of grids searched is 98, and the average length of the search path is 27.9 cm. On a 20 × 20 cm map, the fused algorithm improves the shortcomings of both algorithms and can smoothly avoid obstacles to find the global optimal path. The fuzzy PID control algorithm's convergence time is 0.042 s, and after adding the load of external forces, the fuzzy PID controller does not experience significant turbulence. The results indicate that the robot controlled by the adaptive fuzzy PID controller has stability and effectiveness in path correction control.