複雑な倉庫レイアウトにおける強化学習ベースの倉庫ロボットナビゲーションアルゴリズムの研究
Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout
PPOとダイクストラ法を組み合わせたPP-D手法を提案し、複雑な倉庫内でロボットが高精度かつリアルタイムに最適経路を計画・意思決定できることを示した論文。
著者: Keqin Li, Lipeng Liu, Jiajing Chen, Dezhi Yu, Xiaofan Zhou, Ming Li, Congyu Wang, Zhao Li
分類: cs.RO, cs.AI
原文アブストラクト
In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses Dijkstra algorithm to plan the global optimal path, thus ensuring high navigation accuracy and significantly improving the efficiency of path planning. Specifically, PPO enables robots to quickly adapt and optimize action strategies in dynamic environments through its stable policy updating mechanism. Dijkstra's algorithm ensures global optimal path planning in static environment. Finally, through the comparison experiment and analysis of the proposed framework with the traditional algorithm, the results show that the PP-D method has significant advantages in improving the accuracy of navigation prediction and enhancing the robustness of the system. Especially in complex warehouse layout, PP-D method can find the optimal path more accurately and reduce collision and stagnation. This proves the reliability and effectiveness of the robot in the study of complex warehouse layout navigation algorithm.
