对比三种路径规划方法，发现启发式策略在农田采样中最快最准。
Evaluating Path Planning Strategies for Efficient Nitrate Sampling in Crop Rows
- 用无人机影像与GPS定位，结合三种路径算法指导机器人作业。
- 启发式搜索仅需0.28毫秒，成功率100%，远超深度强化学习模型。
- 适合追求高效稳定的精准农业应用，尤其在结构化农田中。
本文提出一种融合无人机影像生成的高分辨率正射影像图与基于GPS的全局导航的流程，用于引导履带式地面机器人。评估了三种路径规划策略：A*图搜索、深度Q网络（DQN）模型和启发式搜索，并在真实模拟环境中对比其规划时间与成功率。实验结果表明，启发式搜索规划时间最快（0.28毫秒），成功率100%；A*方法表现接近最优；而尽管DQN模型具备适应性，但规划延迟较长且偶发非最优路径。结果表明，在几何约束明显的作物行环境中，确定性规则方法更具优势，为未来混合策略在精准农业中的应用奠定基础。
原文摘要 · Abstract (English)
This paper presents a pipeline that combines high-resolution orthomosaic maps generated from UAS imagery with GPS-based global navigation to guide a skid-steered ground robot. We evaluated three path planning strategies: A* Graph search, Deep Q-learning (DQN) model, and Heuristic search, benchmarking them on planning time and success rate in realistic simulation environments. Experimental results reveal that the Heuristic search achieves the fastest planning times (0.28 ms) and a 100% success rate, while the A* approach delivers near-optimal performance, and the DQN model, despite its adaptability, incurs longer planning delays and occasional suboptimal routing. These results highlight the advantages of deterministic rule-based methods in geometrically constrained crop-row environments and lay the groundwork for future hybrid strategies in precision agriculture.
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