Multi-objective task allocation for electric harvesting robots: a hierarchical route reconstruction approach

2025-09-13

AI Summary

In orchard harvesting using multiple electric robots, jointly optimizing makespan and energy consumption is challenging due to realistic constraints—such as load-dependent velocity variation and limited battery capacity. Method: This paper formally defines the multi-objective Agricultural Multi-Electric-Robot Task Allocation (AMERTA) problem for the first time. It proposes a hierarchical path reconstruction framework featuring a hierarchical encoding structure, a two-phase initialization mechanism, and dedicated path reconstruction operators to enhance solution-space exploration. Furthermore, it designs the Hybrid Hierarchical Path Reconstruction Algorithm (HRRA), integrating task-sequence optimization with a multi-objective evolutionary mechanism. Results: Evaluated on 45 benchmark instances, HRRA consistently outperforms seven state-of-the-art algorithms. Statistical validation via Wilcoxon and Friedman tests confirms its significant superiority in both computational efficiency and robustness.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This paper defines the multi-objective agricultural multi-electrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi-robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.

Problem

• Optimizing multi-robot task allocation for orchard harvesting
• Balancing makespan and energy consumption under practical constraints
• Addressing load-dependent speed variations and battery limitations

Innovation

• Hierarchical route reconstruction algorithm
• Dual-phase initialization method
• Specialized route reconstruction operators