Warehousing multi-AGV path planning method based on improved BP neural network

Abstract

The invention discloses a warehousing multi-AGV path planning method based on an improved BP neural network. The method is characterized by comprising the following steps: acquiring point cloud data of a warehousing environment and constructing a grid map; acquiring real-time locations of all AGVs for path planning at the current moment and corresponding target locations thereof, inputting the locations into a trained BP neural network model, and randomly selecting an AGV as a first input to obtain a moving state of the AGV; repeating the above operations for all AGVs whose moving states are not determined at the current moment, till obtaining the moving states of all the AGVs; if all the AGVs for path planning at the current moment reach the target locations, outputting complete paths ofall the AGVs for path planning at the current moment; otherwise, setting next moment as the current moment, and repeating the above operations. The method greatly simplifies the structure of the neural network, improves the accuracy of a learner, simultaneously improves the convergence speed, avoids over-fitting problems, and effectively improves the efficiency of warehousing operations.

Description

technical field [0001] The invention belongs to the technical field of robots, and more specifically relates to a path planning method for warehouse multi-AGVs based on an improved BP neural network. Background technique [0002] AGV (Automated Guided Vehicle), that is, automatic guided vehicle, refers to a transport vehicle equipped with automatic guidance devices such as electromagnetic or optical, capable of driving along a prescribed guiding path, and having safety protection and various transfer functions. AGV can realize the full automation of item handling and loading and unloading process through its own automatic loading and unloading mechanism and navigation device. Its own automatic navigation system ensures that the AGV can drive automatically without manual traction, and sends the shelves storing the goods from the parking position (starting point) to the operating platform (destination). [0003] AGV is a logistics transportation method with high efficiency, h...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a storage multi-AGV path planning method based on the improved BP neural network. A correction coefficient is added to the cost function of the network to speed up the convergence speed, so as to improve the accuracy and calculation efficiency of multi-AGV path planning in warehousing, thereby solving the problem of low efficiency of logistics operations caused by poor path planning of multi-AGV warehousing in the prior art. costly technical issues

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