Route Fragmentation Based on Resource-centric Prioritisation for Efficient Multi-Robot Path Planning in Agricultural Environments

TL;DR

This paper introduces resource-centric route fragmentation algorithms for multi-robot path planning in dense agricultural environments, significantly improving throughput and operational efficiency over existing agent-centric methods.

Contribution

It proposes two variants of the Fragment Planner that leverage route fragmentation for better conflict resolution in resource contention scenarios, advancing multi-robot planning in agriculture.

Findings

Achieved 95% of optimal task throughput in simulations.

Outperformed baseline algorithms like PP and PBS in throughput.

Demonstrated effectiveness in a 3.6km polytunnel environment.

Abstract

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.