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.

翻译：传统线性作物布局为拖拉机优化设计，却给机器人导航带来急转弯、长距离行驶和感知混淆等问题。我们提出一种以机器人为中心的方形螺旋布局，配备中央轨道线，可实现更简洁的运动路径和更高效的覆盖。为充分利用此几何结构，我们开发了融合DH-ResNet18航点回归、像素-里程计映射、A*路径规划与模型预测控制（MPC）的导航系统。仿真实验表明：在500个航点的任务中，螺旋布局相比线性布局可缩短路径长度达28%，航点任务执行速度提升约25%；而全场覆盖性能与优化的线性U型转向策略相当。多机器人研究显示，在规则约束的螺旋图结构上能实现高效协同——在我们的实验设置中，贪婪分配器比匈牙利算法分配策略的批次完成时间降低33-37%。这些成果凸显了通过重构农田几何形态以适应自主农业需求的巨大潜力。