Reinforcement Learning Based Pallet Loading Algorithm and its Application to a Real Manipulator System
- Resource Type
- Conference
- Authors
- Kang, Seong Woo; Min, Ye Rin; Choi, Kyuwon; Ahn, Woo Jin; Baek, Sang Ryul; Choi, Dae Woo; Lim, Myo Taeg
- Source
- 2023 20th International Conference on Ubiquitous Robots (UR) Ubiquitous Robots (UR), 2023 20th International Conference on. :115-118 Jun, 2023
- Subject
- Robotics and Control Systems
Simulation
Loading
Reinforcement learning
Manipulators
Complexity theory
Pallets
- Language
Manufacturers pallet loading problem (MPLP) aims to fit the maximum number of boxes into a fixed-size pallet capacity. Solving MPLP can be time-consuming due to its complexity, leading to the use of heuristic methods which may not produce optimal results. This paper proposes a pallet loading algorithm using reinforcement learning to find the optimal solution. Simulation results indicate that the proposed method utilizes the given pallet space more efficiently than the existing heuristic methods. In addition, we introduce a real-life automatic pallet loading system and demonstrate the effectiveness of the proposed algorithm.