Potential Fields Guided Deep Reinforcement Learning for Optimal Path Planning in a Warehouse
- Resource Type
- Conference
- Authors
- Ren, Jing; Huang, Xishi
- Source
- 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE) Control Science and Systems Engineering (ICCSSE), 2021 IEEE 7th International Conference on. :257-261 Jul, 2021
- Subject
- Robotics and Control Systems
Costs
Simulation
Transportation
Training data
Reinforcement learning
Systems engineering and theory
Path planning
deep reinforcement learning
deep learning
potential fields
path planning
- Language
Using mobile robots for transportation in a warehouse is becoming more and more common. Compared with human staff, these robots can handle the goods more accurately and more efficiently. Using robots can greatly reduce the operation cost of a warehouse. Optimal path planning for these robots can reduce the transportation time, guarantee the safety of the people in the warehouse, and reduce the goods delivery time and increase daily output. In this paper, we propose an optimal path planning algorithm for the mobile robot using deep reinforcement learning (DRL). Potential fields are employed to guide to collect better quality training data to improve data efficiency. The simulation results have shown that DRL can successfully reach the goal position and avoid collision with the obstacles using the potential fields guided trail-and-error method.