Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning
研究了自动化集装箱码头水平运输区自动导引车的防冲突路径规划问题，构建节点网络并建立整数规划模型，提出MADDPG方法求解，通过数值实验验证了模型和算法的有效性。
AGV conflict prevention path planning is a key factor to improve transportation cost and operation efficiency of the container terminal. This paper studies the anti-conflict path planning problem of Automated Guided Vehicle (AGV) in the horizontal transportation area of the Automated Container Terminals (ACTs). According to the characteristics of magnetic nail guided AGVs, a node network is constructed. Through the analysis of two conflict situations, namely the opposite conflict situation and same point occupation conflict situation, an integer programming model is established to obtain the shortest path. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is proposed to solve the problem, and the Gumbel-Softmax strategy is applied to discretize the scenario created by the node network. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm.