Selecting Subgoal for Social AGV Path Planning by Using Reinforcement Learning

Automated Guided Vehicles (AGVs) can be widely used in many applications. In a dynamic environment where humans and machines coexist, it is expected to encounter traffic jams, queuing, overtaking, comity, and many other diverse, dynamic social interaction. However, capturing the characteristics of the social interactions among passages for path planning is a complex problem. It is crucial to realize the application of AGVs. Dealing with such a complex dynamic environment thus becomes a primary subject of navigation. However, in facing stop-and-go crowds in shopping malls or traffic jams, the past navigation methods cannot effectively cope with the navigation task. Therefore, we propose a hierarchical reinforcement learning framework named MTON, which collects historic environmental statistics to analyze social interactions and periodically selects a promising navigation subgoal to complete the navigation. Our model introduces the idea of interchanging between waiting and moving. Thus it can cope with stop-and-go dynamic obstacles and find a way to break through the barriers to the final goal.