融合深度强化学习与模糊逻辑，实现四轮独立转向驱动机器人高效安全导航。
Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
- 分层决策：高层用DRL规划路径，底层用模糊逻辑保证机械可行性。
- 仿真与实测均证明导航更稳定，比纯DRL方案减少异常行为。
- 适合工业场景中复杂环境下的四轮独立驱动机器人部署。
本文提出一种面向四轮独立转向与驱动（4WISD）系统的自主导航分层决策框架。该方法将深度强化学习（DRL）用于高层导航决策，生成全局运动指令；同时结合模糊逻辑控制器，在底层执行中强制满足运动学约束，防止机械应力与轮胎打滑。仿真实验表明，该框架在训练效率与系统稳定性方面优于传统导航方法，且相较于纯DRL方案显著减少振荡与不规则行为。真实场景验证进一步确认其在动态工业环境中的安全、有效导航能力。整体上，该工作为4WISD移动机器人在复杂真实场景中的部署提供了可扩展、可靠的技术方案。
原文摘要 · Abstract (English)
This paper presents a hierarchical decision-making framework for autonomous navigation in four-wheel independent steering and driving (4WISD) systems. The proposed approach integrates deep reinforcement learning (DRL) for high-level navigation with fuzzy logic for low-level control to ensure both task performance and physical feasibility. The DRL agent generates global motion commands, while the fuzzy logic controller enforces kinematic constraints to prevent mechanical strain and wheel slippage. Simulation experiments demonstrate that the proposed framework outperforms traditional navigation methods, offering enhanced training efficiency and stability and mitigating erratic behaviors compared to purely DRL-based solutions. Real-world validations further confirm the framework's ability to navigate safely and effectively in dynamic industrial settings. Overall, this work provides a scalable and reliable solution for deploying 4WISD mobile robots in complex, real-world scenarios.
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