A segment anchoring-based balancing algorithm for agricultural multi-robot task allocation with energy constraints

TL;DR

This paper introduces SABA, a novel algorithm for efficient multi-robot task scheduling in smart farming, effectively managing energy constraints and dynamic loads to optimize harvesting operations.

Contribution

The paper presents a new segment anchoring-based balancing algorithm that addresses energy constraints and dynamic load resets in multi-robot task allocation, outperforming existing methods.

Findings

SABA outperforms 6 state-of-the-art algorithms in solution quality.

It effectively manages energy constraints and dynamic load resets.

Demonstrated on real-world and benchmark instances.

Abstract

Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto-optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi-trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring-based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting-based rebalancing mechanism is responsible for the fine-grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real-world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state-of-the-art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi-robot task allocation problem under energy constraints.