用螺旋布局优化农田设计，让农机更高效自动作业。
Crop Spirals: Re-thinking the field layout for future robotic agriculture
- 采用中心导轨的方形螺旋布局，简化机器人路径规划
- 仿真显示路径缩短28%，任务执行快25%，覆盖效率相当
- 多机协同效果佳，调度效率比传统方法提升33%-37%
传统线性种植布局虽适合拖拉机，却限制了机器人导航，导致转弯困难、行程长、感知混淆。本文提出以机器人为中心的带中央导轨的方形螺旋布局，实现更简洁运动与更高覆盖率。为此构建了包含DH-ResNet18姿态预测、像素到里程计映射、A*规划和模型预测控制（MPC）的导航系统。仿真结果表明，该布局在500个航点任务中路径最短减少28%，执行时间加快约25%，全田覆盖性能接近优化线性回形穿行策略。多机器人实验显示，在规则约束的螺旋图上使用贪心分配器，批处理完成时间比匈牙利算法降低33%-37%。结果表明，重新设计田间几何结构可显著提升自主农业潜力。
原文摘要 · Abstract (English)
Conventional linear crop layouts, optimised for tractors, hinder robotic navigation with tight turns, long travel distances, and perceptual aliasing. We propose a robot-centric square spiral layout with a central tramline, enabling simpler motion and more efficient coverage. To exploit this geometry, we develop a navigation stack combining DH-ResNet18 waypoint regression, pixel-to-odometry mapping, A* planning, and model predictive control (MPC). In simulations, the spiral layout yields up to 28% shorter paths and about 25% faster execution for waypoint-based tasks across 500 waypoints than linear layouts, while full-field coverage performance is comparable to an optimised linear U-turn strategy. Multi-robot studies demonstrate efficient coordination on the spirals rule-constrained graph, with a greedy allocator achieving 33-37% lower batch completion times than a Hungarian assignment under our setup. These results highlight the potential of redesigning field geometry to better suit autonomous agriculture.
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