Research on Real-Time Planning and Control of Unmanned Cargo Trolley Based on Improved Artificial Potential Field and Model Prediction and Control Algorithm
In view of the avoidance path planning and tracking control problem in the presence of multiple types of dynamic obstacles and complex working conditions,this paper takes the unmanned cargo vehicle in the factory as the research object,and proposes an improved artificial potential field algorithm based on multiple types of dynamic environments.A model predictive tracking control algorithm with adaptive constraints is proposed to address the difficulty of controlling unmanned cargo vehicles under complex load conditions.By improving the obstacle repulsion potential field model,the problem of unreachable targets caused by the extremely large gravitational force in critical situations is solved.Through fuzzy control,the weight coefficients of the unmanned vehicle path tracking controller are optimized to deal with the uncertainty of the model predictive control algorithm under complex conditions and effectively control the oscillation problem of the vehicle during tracking.Finally,the effectiveness of the designed improved algorithm is verified in the unmanned vehicle path planning and trajectory tracking intelligent control system,allowing the unmanned cargo vehicle to avoid obstacles with different parameters and perform effective tracking control.
artificial potential field methodmodel predictive and controldynamic avoidancepath planning
万方数据
维普
