Multi-AGV path planning method based on hop-point pathfinding and cooperative obstacle avoidance

Abstract

The invention discloses a multi-AGV path planning method based on hop-point pathfinding and cooperative obstacle avoidance. Each grid area is used as a path node; the initial running trajectory is planned for the AGV according to the jump point pathfinding algorithm; the various conflicts that may be encountered during the operation of the AGV are handled according to the reactive cooperative obstacle avoidance method. Through the design and planning of the site map using the jump point pathfinding algorithm and the collaborative obstacle avoidance method, the distributed paths of multiple AGVs are obtained, so that each AGV can perform the assigned sorting tasks without conflict, and effectively improve the work efficiency of distributed multiple AGVs. Under the condition of guaranteeing the shortest path, the method of the invention has the advantages of low calculation and communication cost of the algorithm, and small calculation and management burden of the main control. The method is feasible and efficient, and can provide a complete and efficient solution for the distributed multi-AGV scheduling of intelligent warehouses. Program.

Description

technical field [0001] The invention belongs to the technical field of multi-agent path planning, and relates to a path planning task in distributed automatic sorting of multiple automatic guided vehicles in a logistics system. In particular, it relates to a multi-AGV path planning method based on hop-point pathfinding and cooperative obstacle avoidance. Background technique [0002] The arrival of Industry 4.0 is both an opportunity and a challenge for my country. my country has the largest number of robots in the world, but the use of robots is low, and robots entering smart warehouses are an application direction that is in great demand. At the same time, due to practical factors such as the gradual shortage of land resources in my country and the sharp rise in labor costs, the traditional logistics industry is severely restricted. Therefore, with the support of the Internet of Things, cloud computing, big data, etc., along with the development of smart factories and au...

AI Technical Summary

Problems solved by technology

[0005] Only pursue the shortest path length for single-objective scheduling, which takes a long time in large map scenarios, and does not consider path smoothing and other processing, resulting in too many turning points, redundant nodes, or additional path smoothing processing, etc.

This will cause problems such as low system efficiency, difficult control, machine wear, and high storage pressure.

Moreover, most of the control systems adopt centralized control, which gathers all information in the host computer of the main agent, which is a layered top-down control method. AGV only needs to passively receive control, which is simple and easy, but The AGV needs to communicate frequently with the center, and the management center is facing greater management and computing pressure

[0006] At the same time, most of the current solutions have good performance in scenarios with a small number of AGVs. However, as the number of AGVs increases, the disadvantages of the algorithm will gradually be revealed, and the inefficient conflict resolution strategy is difficult to cope with frequent occurrences caused by AGVs sharing network paths and competing for resources. Complex conflict deadlock, AGV is often in a waiting state, wasting a lot of precious time

In addition, there is also the problem of decision-making information lag: planning according to the previous pre-judgment conflicts lags behind the actual situation in the operation process

And it is difficult to solve the algorithm in the high-density AGV scene

These will lead to low operating efficiency of each AGV, resulting in poor algorithm availability and flexibility

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