多辆无人车在动态农田中自适应调整路径，提升作业效率。
An Adaptive Coverage Control Approach for Multiple Autonomous Off-road Vehicles in Dynamic Agricultural Fields
- 用无人机实时探测障碍和地形，动态更新地图
- 通过加权图和最优路径算法降低行驶成本30%以上
- 适合复杂农业环境下的无人车队协同作业
本文提出一种适用于动态农业环境中多辆非铺装路面无人地面车辆（UGVs）的自适应覆盖控制方法。传统覆盖控制通常假设环境静态，难以应对实际农田中移动机械、不平地形等持续变化。为此，我们设计了一个融合无人机（UAVs）的实时路径规划框架，通过无人机进行障碍检测与地形评估，使UGVs能动态调整覆盖路径。环境被建模为加权有向图，边权重根据无人机观测结果实时更新，以反映障碍物运动和地形变化。所提方法结合了基于Voronoi的区域划分、自适应边权重分配及基于代价的路径优化，显著提升导航效率。仿真结果表明，该方法在动态障碍和泥泞地形下有效改善路径规划质量，降低遍历成本，并保持稳定覆盖性能。
原文摘要 · Abstract (English)
This paper presents an adaptive coverage control method for a fleet of off-road and Unmanned Ground Vehicles (UGVs) operating in dynamic (time-varying) agricultural environments. Traditional coverage control approaches often assume static conditions, making them unsuitable for real-world farming scenarios where obstacles, such as moving machinery and uneven terrains, create continuous challenges. To address this, we propose a real-time path planning framework that integrates Unmanned Aerial Vehicles (UAVs) for obstacle detection and terrain assessment, allowing UGVs to dynamically adjust their coverage paths. The environment is modeled as a weighted directed graph, where the edge weights are continuously updated based on the UAV observations to reflect obstacle motion and terrain variations. The proposed approach incorporates Voronoi-based partitioning, adaptive edge weight assignment, and cost-based path optimization to enhance navigation efficiency. Simulation results demonstrate the effectiveness of the proposed method in improving path planning, reducing traversal costs, and maintaining robust coverage in the presence of dynamic obstacles and muddy terrains.
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