Adaptive Robot Motion Planning for Smart Manufacturing Based on Digital Twin and Bayesian Optimization-Enhanced Reinforcement Learning

Advanced motion planning is crucial for safe and efficient robotic operations in various scenarios of smart manufacturing, such as assembling, packaging and palletizing. Compared to traditional motion planning methods, Reinforcement Learning (RL) shows better adaptability to complex and dynamic working environments. However, the training of RL models is often time-consuming and the determination of well-behaved reward function parameters is challenging. To tackle these issues, we propose an adaptive robot motion planning approach based on digital twin and reinforcement learning. The core idea is to adaptively select geometry-based or RL-based method for robot motion planning through a real-time distance detection mechanism, which can reduce the complexity of RL model training and accelerate the training process. In addition, we integrate Bayesian Optimization within RL training to refine the reward function parameters. We validate the approach with a Digital Twin enabled robot system through five kinds of tasks (Pick and Place, Drawer Open, Light Switch, Button Press, Cube Push) in dynamic environments. Experiment results show that our approach outperforms traditional RL-based method with improved training speed and guaranteed task performance. This work contributes to the practical deployment of adaptive robot motion planning in smart manufacturing.