Some specialty crops, such as strawberries and table grapes, are harvested by large crews of pickers who spend significant amounts of time carrying empty and full (with the harvested crop) trays. A step toward increasing harvest automation for such crops is to deploy harvest-aid robots that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers’ non-productive walking times. To that end, this work addresses human-robot collaboration modeling in a harvesting context. First, a modeling framework for all-manual and robot-aided harvesting was developed, which can be used for off-line simulation by system designers, but also as a representation model for robot control, during real-time operation. To serve both functions, the framework utilizes hybrid systems to model picker and robot activities. Finite state machines model discrete operating states, and difference equations describe motion and mass transfer within each discrete state. To capture the variability in human behavior and performance during harvesting, the human activity model utilizes stochastic parameters (e.g., picking time, walking speed) that can be estimated by measurements during harvesting. The stochastic model does not require direct yield measurements, which are not available for most specialty crops. Second, a stochastic simulator was developed based on the developed model. For a given field and crew size, the simulator samples all stochastic parameters to generate many instances of the harvest operation, and estimates metrics such as pickers’ non-productive time and harvest operation efficiency. Part II of this work presents the calibration and evaluation of the simulator based on field data, and a case study that evaluates the effect of various robot scheduling algorithms on harvest efficiency.

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.