评估作物行中高效硝酸盐采样的路径规划策略
Evaluating Path Planning Strategies for Efficient Nitrate Sampling in Crop Rows
AI总结：
本文比较了A*、DQN和启发式搜索三种路径规划策略在作物行硝酸盐采样中的表现，发现启发式搜索最快且成功率100%，为精准农业混合策略奠定基础。
摘要：
本文提出了一条流水线，将无人机（UAS）影像生成的高分辨率正射镶嵌地图与基于GPS的全局导航相结合，以引导一台滑移转向地面机器人。我们评估了三种路径规划策略：A*图搜索、深度Q学习（DQN）模型和启发式搜索，并在真实感仿真环境中以规划时间和成功率为基准对它们进行了对比测试。实验结果表明，启发式搜索实现了最快的规划时间（0.28毫秒）和100%的成功率，而A*方法提供了接近最优的性能，DQN模型尽管具有适应性，但会产生更长的规划延迟和偶尔的次优路径。这些结果凸显了确定性基于规则的方法在几何受限的作物行环境中的优势，并为精准农业中未来的混合策略奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
