Abstract Manual harvesting of fresh-market crops like strawberries is very labor-intensive. Apart from picking fruits, pickers spend significant amounts of time carrying full trays to a collection station at the edge of the field. Small teams of harvest-aid robots that help large picking crews by transporting empty and full trays can increase harvest efficiency by reducing pickers’ non-productive walking times. However, robot sharing among the crew may introduce non-productive waiting delays between the time a tray becomes full and when a robot arrives to collect it. Reactive robot scheduling cannot eliminate mean waiting times because pickers must wait for a robot to travel the distance from the collection station to them. Predictive scheduling is better suited to this task, because if the time and location that a pickers’ tray will fill are known to the scheduler in advance, a robot can start moving toward that location before the tray becomes full; hence, waiting times due to robot travel can be reduced or eliminated. In this paper, dynamic predictive scheduling was modeled for teams of robots carrying trays during manual harvesting. The times and locations of the tray-transport requests were assumed to be known exactly (deterministic predictions). Near-optimal scheduling was implemented to provide efficiency upper-bounds for any predictive scheduling algorithms that incorporate uncertainty in the predictive requests. Robot-aided harvesting was simulated using manual-harvest data collected from a commercial picking crew. Scheduling performance was studied as a function of the number of robots – for a given crew size – with robot speed as a parameter. Additionally, the effect of the earliness of the availability of the predictions on performance was studied. Experimental results showed that both reactive and predictive scheduling did not improve the mean non-productive time significantly relative to manual harvesting, when only four robots were used. Actually, deploying fewer than four robots led to worse non-productive time. However, introducing five to eight robots decreased mean non-productive time drastically, and when ten or more robots were used, non-productive time was reduced by 64.6% (reactive scheduling) and up to 93.7% (predictive scheduling) with respect to all-manual non-productive time. The efficiency increases were 15% and 24%, respectively. It was also verified that reactive dispatching always performed worse than deterministic predictive scheduling. Also, when the robot-to-picker ratio was larger than approximately 1:3, the waiting time and efficiency plateaued, i.e., did not improve further, regardless of how early the prediction was available to the scheduler. The reason is that the mean waiting time is lower bounded by the sum of mean travel time and tray exchange time, which are both constant. Although the above results represent upper-bounds for performance – since predictions were perfect - they indicate that tray-transport robots acting as harvest aids can increase harvesting efficiency significantly when scheduled properly.

中文翻译：

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作物运输协同机器人作为收割辅助工具的确定性预测动态调度

摘要 人工收获草莓等新鲜市场作物是非常劳动密集型的。除了采摘水果外，采摘者还花费大量时间将装满托盘的托盘运送到田地边缘的收集站。通过运输空托盘和满托盘来帮助大型采摘人员的小型收获辅助机器人团队可以通过减少采摘者的非生产性行走时间来提高收获效率。然而，工作人员之间的机器人共享可能会在托盘变满和机器人到达收集托盘之间引入非生产性的等待延迟。反应式机器人调度无法消除平均等待时间，因为拣货员必须等待机器人从收集站到达他们的距离。预测性调度更适合这项任务，因为如果调度程序提前知道拣货员托盘装满的时间和位置，机器人就可以在托盘装满之前开始向该位置移动；因此，可以减少或消除由于机器人移动而导致的等待时间。在本文中，对手动收割期间搬运托盘的机器人团队进行了动态预测调度建模。假设托盘运输请求的时间和位置是准确已知的（确定性预测）。实施了近乎最优的调度，以便为在预测请求中包含不确定性的任何预测调度算法提供效率上限。使用从商业采摘人员收集的手动收获数据模拟机器人辅助收获。调度性能被研究为机器人数量的函数——对于给定的船员规模——以机器人速度作为参数。此外，还研究了预测可用性的早期对性能的影响。实验结果表明，当仅使用四个机器人时，相对于手动收割，反应式和预测式调度都没有显着改善平均非生产时间。实际上，部署少于四个机器人会导致更糟的非生产时间。然而，引入 5 到 8 个机器人后，平均非生产时间显着减少，当使用 10 个或更多机器人时，非生产时间减少了 64.6%（反应式调度）和高达 93.7%（预测式调度）。 - 手动非生产时间。效率分别提高了 15% 和 24%。还验证了反应式调度的性能总是比确定性预测性调度差。此外，当机器人与拣货员的比率大于大约 1:3 时，等待时间和效率趋于稳定，即不会进一步提高，无论调度程序有多早获得预测。原因是平均等待时间是平均旅行时间和托盘交换时间之和的下限，两者都是常数。尽管上述结果代表了性能的上限——因为预测是完美的——但它们表明，托盘运输机器人作为收割辅助工具可以在适当安排时显着提高收割效率。当机器人与拣货员的比率大于大约 1:3 时，等待时间和效率趋于平稳，即不会进一步提高，无论调度程序有多早获得预测。原因是平均等待时间是平均旅行时间和托盘交换时间之和的下限，两者都是常数。尽管上述结果代表了性能的上限——因为预测是完美的——但它们表明，托盘运输机器人作为收割辅助工具可以在适当安排时显着提高收割效率。当机器人与拣货员的比率大于大约 1:3 时，等待时间和效率趋于平稳，即不会进一步提高，无论调度程序有多早获得预测。原因是平均等待时间是平均旅行时间和托盘交换时间之和的下限，两者都是常数。尽管上述结果代表了性能的上限——因为预测是完美的——但它们表明，托盘运输机器人作为收割辅助工具可以在适当安排时显着提高收割效率。原因是平均等待时间是平均旅行时间和托盘交换时间之和的下限，两者都是常数。尽管上述结果代表了性能的上限——因为预测是完美的——但它们表明，托盘运输机器人作为收割辅助工具可以在适当安排时显着提高收割效率。原因是平均等待时间是平均旅行时间和托盘交换时间之和的下限，两者都是常数。尽管上述结果代表了性能的上限——因为预测是完美的——但它们表明，托盘运输机器人作为收割辅助工具可以在适当安排时显着提高收割效率。