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Modern commercial orchards are increasingly adopting trees of SNAP architectures (Simple, Narrow, Accessible, and Productive) as the fruits on such trees are, in general, more easily reachable by human or robotic harvesters. This paper presents a methodology that utilizes three dimensional (3D) digitized computer models of high-density pear and cling-peach trees, and fruit positions to quantify the linear fruit reachability (LFR) of such trees, i.e., their reachability by telescoping robot arms. Robot-canopy noninterference geometric constraints were introduced in the simulator, to determine the closest position of the arms' base frames with respect to the trees, inside an orchard row. Also, design constraints for such arms, such as maximum reach, size and type of the gripper, and range of approach directions, were introduced to estimate the effect of each of these constraints on the LFR. Simulations results showed that 85.5% of pears were reachable after harvesting consecutively, at three different approach angles ('passes') with a gripper of size 11 cm and an arm extension of 150 cm. For peaches, after three passes, 83.5% were reachable with a gripper size of 11 cm and an arm extension of 200 cm. LFR increased as the gripper's size approached the maximum fruit size and decreased thereafter. Also, retractive grippers on linear arms yielded more fruit compared to vacuum-tube type grippers. Overall, the results suggested that telescoping arms can be used to harvest certain types of SNAP-style trees. Also, this methodology can be used to guide the design of robotic harvesters, as well as the canopy management practices of fruit trees.

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Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This study presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this study can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.

Safe navigation of labour-aiding robots in commercial orchards will rely on accurate and continuous worker localisation. In this work, an ultra-wideband radio-based system localises a worker via trilateration of four range measurements between antennas on a vehicle and an antenna carried on the worker's belt. Performance results are presented from measurements inside 'work zones' around the vehicle, in open space and in an orchard. At walking speed in open space, when body placement allowed full line-of-sight (LOS) between belt and vehicle antennas, position estimate availability was 99.7% and the distance root mean square error (DRMS) was 57.9 cm. Completely blocked LOS resulted in signal outages and unacceptable performance (11.1% availability; 819.7 cm DRMS). In the orchard, full-LOS performance was similar to that in open-space: 99.3% availability and 63.4 cm DRMS error bound. Orchard trees enabled multipath signal propagation, so blocked-LOS performance was far better than in open-space (60.2% availability; 123.6 cm DRMS). Antenna motion effects were studied in open-space and orchard experiments without body interference. Motion introduced non-collocation errors (individual ranges measured at slightly different positions); DRMS error in open space and orchard were 1.6 and 2.2 times larger than respective static errors. In all experiments the 95 th percentiles of the errors were almost twice as large as the DRMS errors. Sporadic large errors and signal outages could be addressed by two belt antennas and filtering. The results indicate that radio ranging offers a practicable approach to orchard worker localisation relative to a nearby vehicle operating at slow walking speeds.