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

TL;DR

This paper introduces an MILP-based scheduling framework for dual-arm robots in plant factories, significantly improving harvesting efficiency and throughput by optimizing task coordination and reachability.

Contribution

It presents a novel MILP framework for dual-arm robot scheduling in strawberry harvesting, enhancing efficiency over single-arm systems through optimized task coordination.

Findings

Dual-arm system nearly doubles efficiency compared to single-arm.

Simulations show 10-20% increase in throughput.

Optimized scheduling reduces stops and improves scalability.

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.