Agricultural environments present high proportions of spatially dense navigation bottlenecks for long-term navigation and operational planning of agricultural mobile robots. The existing agent-centric multi-robot path planning (MRPP) approaches resolve conflicts from the perspective of agents, rather than from the resources under contention. Further, the density of such contentions limits the capabilities of spatial interleaving, a concept that many planners rely on to achieve high throughput. In this work, two variants of the priority-based Fragment Planner (FP) are presented as resource-centric MRPP algorithms that leverage route fragmentation to enable partial route progression and limit the impact of binary-based waiting. These approaches are evaluated in lifelong simulation over a 3.6km topological map representing a commercial polytunnel environment. Their performances are contrasted against 5 baseline algorithms with varying robotic fleet sizes. The Fragment Planners achieved significant gains in throughput compared with Prioritised Planning (PP) and Priority-Based Search (PBS) algorithms. They further demonstrated a task throughput of 95% of the optimal task throughput over the same time period. This work shows that, for long-term deployment of agricultural robots in corridor-dominant agricultural environments, resource-centric MRPP approaches are a necessity for high-efficacy operational planning.

翻译：农业环境在农业移动机器人的长期导航与作业规划中存在高比例的空间密集导航瓶颈。现有以智能体为中心的多机器人路径规划方法从智能体而非竞争资源的角度解决冲突。此外，此类竞争点的密度限制了空间交错的能力——该概念是许多规划器实现高吞吐量的基础。本研究提出两种基于优先级的分段规划器变体作为资源中心的多机器人路径规划算法，利用路由分段实现部分路径推进，并限制二元等待机制的影响。这些方法在代表商业连栋温室环境的3.6公里拓扑地图上进行了终身仿真评估，其性能与五种基线算法在不同机器人车队规模下进行对比。相较于优先级规划与基于优先级的搜索算法，分段规划器实现了显著的吞吐量提升，并在相同时段内达到最优任务吞吐量的95%。本研究表明，在走廊主导的农业环境中长期部署农业机器人时，资源中心的多机器人路径规划方法是实现高效作业规划的必要条件。