Abstract Harvest-aid robots that transport empty and full trays during manual harvesting of specialty crops such as strawberries or table grapes can increase harvest efficiency, by reducing pickers' non-productive walking times. In Part I of this work, a modeling framework, and a stochastic simulator were presented for all-manual and robot-aided harvesting. This paper reports Part II of our work, which utilized data gathered in two strawberry fields during harvesting, to estimate the stochastic parameters involved in modeling pickers, and evaluate the prediction accuracy of the simulator for all-manual picking. Then, as a case study, non-productive time and harvest efficiency were estimated for robot-aided harvesting, for various picker-robot ratios and three priority-based reactive dispatching strategies for the robots. The simulator predicted the pickers' non-productive time during all-manual harvesting, with 6.4%, 3%, and 1.2% errors for the morning, afternoon, and “all-day” harvesting shifts, respectively. Statistical testing verified that predicted non-productive times followed the same distributions as the measured non-productive times (5% significance level). Simulations robustness was assessed by using morning data to simulate afternoon harvesting and vice-versa: non-productive times distributions were predicted accurately (10% significance level). Robot-aided simulation results – using the calibrated simulator for a 25-picker crew – showed that all-manual harvest efficiencies of 81.8% and 78.2% for morning and afternoon shifts increased to 92% and 86.5%, respectively, when five robots were deployed. Different scheduling policies did not affect efficiency when more than five robots were used, because there were always enough robots to serve pickers' requests immediately. Also, harvest efficiency plateaued when more than five robots were used, as a consequence of the time needed for a robot to travel to a picker.

中文翻译：

---

收获期间人工采摘者和作物运输机器人的协作——第二部分：模拟器评估和机器人调度案例研究

摘要 在草莓或鲜食葡萄等特殊作物的人工收获期间运输空托盘和满托盘的收获辅助机器人可以通过减少采摘者的非生产性步行时间来提高收获效率。在这项工作的第一部分中，介绍了用于全手动和机器人辅助收割的建模框架和随机模拟器。本文报告了我们工作的第二部分，该部分利用收获期间在两个草莓田中收集的数据来估计采摘机建模所涉及的随机参数，并评估模拟器对全手动采摘的预测准确性。然后，作为案例研究，估计了机器人辅助收割的非生产时间和收割效率，针对各种拣货机器人比率和机器人的三种基于优先级的反应调度策略。模拟器预测采摘者在全手动收获期间的非生产时间，上午、下午和“全天”收获班次的误差分别为 6.4%、3% 和 1.2%。统计检验证实预测的非生产时间遵循与测量的非生产时间相同的分布（5% 显着性水平）。模拟的稳健性是通过使用上午的数据来模拟下午的收获来评估的，反之亦然：准确预测非生产时间分布（10% 显着性水平）。机器人辅助模拟结果——使用 25 名采摘人员的校准模拟器——表明，当部署五个机器人时，上午和下午班次的 81.8% 和 78.2% 的全手动收获效率分别提高到 92% 和 86.5% . 当使用超过五个机器人时，不同的调度策略不会影响效率，因为总是有足够的机器人来立即满足拣货员的请求。此外，当使用超过五个机器人时，收获效率会趋于稳定，这是因为机器人需要时间前往采摘者。