用数学规划优化双臂机器人采收草莓，效率提升近一倍。
Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories
- 构建混合整数线性规划模型，统筹双臂采摘任务调度。
- 仿真显示吞吐量提升10%-20%，停顿次数显著减少。
- 适合植物工厂中高密度草莓采收的自动化系统设计者。
植物工厂种植因能优化资源利用并提高作物产量而广受认可。为进一步提升此类环境下的作业效率，我们提出一种混合整数线性规划（MILP）框架，系统性地调度与协调双臂采摘任务，以预映射的果实位置为基础，最小化整体采收周期。具体而言，针对专用双臂采摘机器人，通过末端执行器姿态覆盖分析，最大化可采摘范围。此外，将双臂配置与单臂车辆性能进行对比，发现在两侧果实密度相近时，双臂系统效率几乎翻倍。大量仿真表明，相比非优化方法，吞吐量提升10%-20%，停顿次数显著减少。这些结果凸显了最优调度策略在提升植物工厂中机器人采收可扩展性与效率方面的优势。
原文摘要 · Abstract (English)
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.
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