基于改进人工势场与模型预测控制算法的无人运货小车实时规划与控制研究

Research on Real-Time Planning and Control of Unmanned Cargo Trolley Based on Improved Artificial Potential Field and Model Prediction and Control Algorithm

针对多类型动态障碍物和复杂工况环境下的避碰路径规划与跟踪控制问题,以厂房无人运货小车为研究对象,提出了一种基于多类型动态环境的改进人工势场算法,并针对复杂负载工况下无人运货小车难以控制的问题提出了自适应约束的模型预测跟踪控制算法。通过改进障碍物斥力势场模型,解决了由临界情况引力极大导致的目标不可达问题;通过模糊控制优化了无人车路径跟踪控制器的权重系数,以应对复杂工况下模型预测控制算法的不确定性,有效解决了小车在跟踪过程中的震荡问题。最后,在搭建的无人车路径规划和轨迹跟踪智能控制系统内验证了所设计改进算法的有效性。无人运货小车能够避开各类参数不同的障碍物,并进行有效的跟踪控制。

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.