Automated Guided Logistics Handling Vehicle Path Routing under Multi-Task Scenarios

In recent years, the application of automated guided vehicles (AGVs) in industrial manufacture has attracted more and more attention. Based on reinforcement learning, an automated guided logistics handling vehicles routing algorithm was proposed under a multi-task scenario. First, the algorithm calculated routing paths for each task node using Q-learning to solve the “curse of dimensionality” problem. Then, with the objective of comprehensive cost considering the time window, the optimal routing sequence was obtained using the depth first search algorithm (DFS). Finally, an example is given to illustrate the algorithm.

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