AGV (Automated Guided Vehicle) route planning method and system based on ant colony algorithm and multi-intelligent agent Q learning

Abstract

The invention discloses an AGV (Automated Guided Vehicle) route planning method and system based on an ant colony algorithm and multi-intelligent agent Q learning, improving the global optimization ability, realizing a case that an AGV learns how to avoid an obstacle in the interaction process by introducing the multi-intelligent agent Q learning into a route planning research of the AGV, and canplay independence and learning capacity of the AGV better. The AGV route planning method and system is characterized in that according to a static environment, carrying out modeling on an AGV operation environment by utilizing a grid method, and setting an initial point and a target point; according to coordinates of the initial point and the target point of the AGV, generating a global optimal route by the ant colony algorithm; enabling the AGV to move towards the target point according to the global optimal route, and when detecting that a dynamic obstacle exists in a minimum distance, carrying out selection of an obstacle avoidance strategy by an environment state corresponding to the multi-intelligent agent Q learning so as to take a corresponding obstacle avoidance action, and after ending obstacle avoidance, returning to an original route to continuously move.

Description

technical field [0001] The invention relates to an AGV path planning technology, in particular to an AGV (Automated Guided Vehicle) path planning method and system based on an improved ant colony algorithm and optimized multi-agent Q learning in a dynamic environment. Background technique [0002] Path planning is an important issue in AGV planning and scheduling. As more and more AGVs are put into use, many new problems have been brought about, such as the establishment of dynamically changing environmental models; the path planning of multiple AGVs and the problem of obstacle avoidance. ; The problem of the learning and intelligence of the multi-AGV system. The existing AGV path planning methods include swarm intelligent bionic algorithm, A*, D* algorithm, etc. Such a single global planning method often cannot achieve the global optimum, and requires high prior knowledge of the environment and requires a large When encountering a complex and dynamic environment, the effic...

AI Technical Summary

Problems solved by technology

Among them, the Q-learning algorithm, as a reinforcement learning algorithm, learns from the environment state to the action mapping. The agent adopts the optimal strategy according to the maximum reward value. The principle of the Q-learning algorithm is easy to understand, easy to combine with reality, and suitable for use in unknown environments. AGV path planning research, but there are more than one agent in a multi-agent system, so when applying Q-learning, it needs to be different from single-agent Q-learning. Multi-agents need to consider the decisions and influences of other agents. When the environment is complex Unknown, when the number of agents is too large, the dimension of the entire state space will increase rapidly, resulting in the disaster of dimensionality

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