A multi-AGV motion planning method, apparatus (500), and system (600). The method comprises: establishing an object model by means of a reinforcement learning method (S110); constructing a neural network model on the basis of the object model, setting an environment in which an AGV group (620) is deployed, and training the neural network model by using the object model of the AGVs in the set environment until obtaining a stable neural network model (S120); setting an action constraint rule (S130); and inputting the current AGV state in the current environment, other AGV states, and a permissible action into the trained neural network model after starting motion planning, obtaining the evaluation indexes of a motion planning result outputted by the neural network model, obtaining, according to the evaluation indexes, an action to be executed of the current AGV, and using the action constraint rule to determine the effectiveness of the action to be executed, so that the current AGV executes an effective action according to a determination result (S140). Therefore, the present application can improve the performance of the multi-AGV motion planning method in a dynamic dense environment.
MULTI-AGV MOTION PLANNING METHOD, APPARATUS, AND SYSTEM