Obstacle Avoidance for Automated Guided Vehicles Based on Deep Reinforcement Learning

Automated Guided Vehicles AGVs play a vital role in enhancing productivity and efficiency within factory environments. However, their safe and effective operation heavily relies on the ability to navigate through complex spaces while avoiding obstacles. The significance of obstacle avoidance in AGV systems is emphasized, considering its impact on ensuring smooth material flow, minimizing collision risks, and optimizing production processes. The existing state of obstacle avoidance applications in factory settings reveals certain limitations and challenges. Current research and industrial implementations often rely on rule-based approaches or predefined paths, which may not adequately adapt to dynamic environments or unexpected obstacles. Additionally, some methods lack the ability to handle diverse obstacle types or efficiently plan optimal paths, leading to sub-optimal navigation or reduced throughput. In response to these challenges, this study proposes a novel approach for dynamic obstacle avoidance of AGVs based on deep reinforcement learning. By leveraging the Deep Deterministic Policy Gradient (DDPG) model, the AGV learns to make real-time decisions and navigate through dynamic obstacles effectively. The integration of deep neural networks with the actor-critic framework enables the AGV to learn and adapt optimal policies for obstacle avoidance in real-time, overcoming the limitations of rule-based methods. Simulation experiments are conducted to validate the performance and feasibility of the proposed approach. The results demonstrate that the DDPG-based method allows the AGV to successfully navigate through both dynamic and static obstacles in a dynamic environment, improving safety and efficiency in intelligent manufacturing applications.