Plant factory cultivation is widely recognized for its ability to optimize resource use and boost crop yields. To fur- ther 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 spe- cialized 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.
@ARTICLE{2507.04240,
author={Yuankai Zhu, Wenwu Lu, Guoqiang Ren, Yibin Ying, Stavros Vougioukas, Chen Peng},
Conference={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories},
year={2025},
doi={https://doi.org/10.48550/arXiv.2507.04240}
}