Traditional path planning methods, such as graph-based algorithms (Dijkstra, A*, D*), sampling-based methods (RRT, RRT*), and artificial potential fields, face significant limitations in complex and dynamic environments. These include high computational cost in dense spaces, lack of optimality guarantees, and susceptibility to local minima. To address these challenges, deep reinforcement learning (DRL) has emerged as a promising alternative, combining deep learning's representation capabilities with reinforcement learning's decision-making strengths.
Key takeaways
- DRL methods are categorized into value-based (e.g., DQN), policy-based, and actor-critic hybrid approaches, each with distinct trade-offs in sample efficiency and stability.
- Representative algorithms were benchmarked on a unified platform, providing practical comparisons for industrial deployment.
- DRL enables end-to-end path planning without explicit environment modeling, adapting to dynamic and unknown scenarios.
- Challenges remain in sample efficiency, reward design, and generalization across diverse environments.
For manufacturing automation, DRL-based path planning offers potential for real-time adaptive navigation of mobile robots and manipulators, reducing reliance on pre-computed maps and improving robustness to changing factory layouts. However, careful algorithm selection and training data are critical for reliable performance.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2026-01-13 · “基于深度强化学习的路径规划算法综述”