Deterministic predictive dynamic scheduling for crop-transport robots acting as harvesting-aids

Manual harvesting of fresh-market crops like strawberries is very labor-intensive. Apart from picking fruits, pickers spend significant amounts of time carrying full traysto a collection station at the edge of the field. Small teams of harvest-aid robots that help large picking crewsby 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 suitedto 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 reducedor 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-boundsfor 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 –and as a function of robot speed. Additionally, the effect of the earliness of the availability of the predictionson performance was studied. Experimental results showed that when only four robots were used, reactive and predictive scheduling did not improve the mean non-productive time significantly. 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-productivetime was reduced by 64.6% (reactive scheduling) and up to 93.7% (predictive scheduling) with respect to all-manual non-productive time. The corresponding efficiency increases were 15% and 24%. It was also verified that reactive dispatching performed was always 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 andtray 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. Download paper here