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

2025-11-21

AI Summary

This paper addresses the electric multi-robot harvesting scheduling problem in smart agriculture, where energy constraints (limited battery capacity) and dynamic load updates jointly induce strong coupling interference among makespan, transportation cost, payload, and remaining energy. To tackle this challenge, we propose the Segmented Anchored Balancing Algorithm (SABA): it reconstructs perturbed robot paths via sequence anchoring while tightly integrating charging decisions with path reoptimization; further, it introduces a proportional splitting and rebalancing mechanism to cooperatively optimize Pareto-optimal solutions across multiple objectives. Evaluated on both real-world agricultural scenarios and standard benchmarks, SABA consistently outperforms six state-of-the-art algorithms in convergence, solution diversity, and robustness. It significantly improves scheduling efficiency and practical deployability for energy-constrained robotic harvesting systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing harvesting schedules for electric agricultural robots with energy constraints
• Managing conflicting objectives of makespan and transportation cost under battery limitations
• Addressing schedule disruptions from mandatory recharging during multi-trip operations

Innovation

• Segment anchoring-based balancing algorithm for task allocation
• Sequential anchoring mechanism reconstructs disrupted robot routes
• Proportional splitting-based rebalancing tunes final solution makespans