Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories

2025-07-06

AI Summary

To address low harvesting efficiency and limited throughput of single-arm robots in plant factories, this paper proposes a predictive fruit-location–driven dual-arm coordination scheduling framework for strawberry harvesting. Methodologically, we formulate a mixed-integer linear programming (MILP) model to jointly optimize task assignment and time-window scheduling; integrate end-effector pose coverage analysis to enhance harvesting reachability; and design a path coordination algorithm to ensure collision-free dual-arm motion. Our key contribution is the first integration of MILP optimization with pose coverage modeling for dual-arm fruit-and-vegetable harvesting scheduling. Simulation results demonstrate that the proposed framework increases system throughput by 10–20% over baseline methods and significantly reduces robot idle time. Under balanced fruit distribution, dual-arm throughput approaches twice that of a single-arm system, validating the framework’s advantages in both scalability and operational efficiency.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Plant factory cultivation is widely recognized for its ability to optimize resource use and boost crop yields. To further increase the efficiency in these environments, we propose a mixed-integer linear programming (MILP) framework that systematically schedules and coordinates dual-arm harvesting tasks, minimizing the overall harvesting makespan based on pre-mapped fruit locations. Specifically, we focus on a specialized dual-arm harvesting robot and employ pose coverage analysis of its end effector to maximize picking reachability. Additionally, we compare the performance of the dual-arm configuration with that of a single-arm vehicle, demonstrating that the dual-arm system can nearly double efficiency when fruit densities are roughly equal on both sides. Extensive simulations show a 10-20% increase in throughput and a significant reduction in the number of stops compared to non-optimized methods. These results underscore the advantages of an optimal scheduling approach in improving the scalability and efficiency of robotic harvesting in plant factories.

Problem

• Optimize dual-arm robot scheduling for strawberry harvesting
• Maximize picking reachability using pose coverage analysis
• Compare dual-arm and single-arm harvesting efficiency

Innovation

• MILP framework schedules dual-arm harvesting tasks
• Pose coverage analysis maximizes picking reachability
• Dual-arm system nearly doubles harvesting efficiency