Methodology Research On Path Planning And Autonomous Obstacle Avoidance For AGV

Path planning is one of the main research contents of automatic guided vehicle navigation system(AGV),which has many applications in the fields of life,high-tech and business.For example,in the field of life,there is electronic map navigation,planning the optimal path based on the degree of urban public road congestion;in the commercial field,there are autonomous cargo trucks;in the high-tech field,there are autonomous obstacle avoidance motion of robots,etc.With the rapid development of networks and computers,path planning has also developed rapidly.However,traditional path planning algorithms still have shortcomings such as uneven paths,many turning points,and long planning times in maps with large map ranges and complex environments.Therefore,this paper conducts an in-depth study on how to plan a smooth and safe obstacle avoidance path with better time cost.In maps with unknown environment and high complexity,using graph-based search algorithms,such as A* algorithm and Dijkstra algorithm,is prone to problems such as many repeated planning times and low operation efficiency in unknown environments;Lack of environmental information can easily lead to low planning efficiency.In this paper,the traditional path planning algorithms such as Dijkstra algorithm and A* algorithm are compared and analyzed,and the advantages and disadvantages of each algorithm and its application are discussed.Finally,the D*Lite algorithm is selected according to the more complex map in this paper,which can be well adapted to unknown maps and dynamic environments.Considering that the D*Lite algorithm has the limitations of low search efficiency and insufficient reliability in a complex environment with a large map range,this paper improves the heuristic function and its evaluation function of the D*Lite algorithm to improve the search efficiency of the algorithm.The simulation research is carried out in various environments,and the feasibility and reliability of the improved algorithm are proved by experiments.The paths planned by conventional path planning algorithms often have problems such as uneven paths,too large turning angles,and too many redundant nodes,which easily increase the complexity of the AGV tracking path algorithm.In this paper,an improved algorithm based on genetic algorithm fusion with B-spline curve is designed.The planned path nodes are combined with the improved genetic algorithm,and the path length,the turning angle in the path are introduced into the fitness function.Plan new paths that avoid obstacles,are smooth and have shorter distances.The control points optimized by genetic algorithm are smoothed by B-spline curve algorithm,and the path with smaller turning angle and easier to operate is obtained.After the path planned by the improved D*Lite algorithm is optimized and smoothed,the path can better meet the requirements of high planning efficiency,smooth path,small turning angle,short path and high-quality path that can avoid obstacles easy to implement in actual AGV operation.