用强化学习优化自动驾驶路径规划，效率逼近最优解。
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles
- 融合强化学习与Google OR-Tools求解路径规划问题。
- Q-Learning平均仅偏离最优解1.2%，性能最佳。
- 适合关注智能路径规划与算法对比的研究者。
本文针对无人地面车辆的全局路径规划（GPP）开展深入研究，以自主采矿采样机器人ROMIE为应用背景。GPP对ROMIE的高效运行至关重要，其本质是求解旅行商问题（TSP），即在矿场中找到覆盖所有采样点的最短路径，显著提升作业效率与成本优势。研究目标是开发、评估并改进一种低成本、可部署的软件与网页应用。通过系统比较Google运筹学（OR-Tools）优化算法，并首次尝试将强化学习技术与之结合，以探索其计算效率与实际应用潜力。结果表明，Q-Learning在多个数据集上平均仅偏离最优解1.2%，表现最优，验证了该方法的有效性与实用性。
原文摘要 · Abstract (English)
We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.
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