Co-robotic harvest-aid platforms: Real-time control of picker lift heights to maximize harvesting efficiency

Abstract

Harvest-aid platforms are used in modern orchards to improve manual harvesting efficiency, safety, and ergonomics. Typically, workers stand at pre-set heights on a platform's multi-level deck, and each worker harvests fruits inside a canopy zone that is defined by the lowest and highest reach of the worker's arms. However, fruit distributions are non-uniform, and worker picking speeds vary, thus generating a mismatch between labor demand (incoming fruit rates) and labor supply (fruit picking rates) in each zone; this mismatch limits platform-based harvesting efficiencies. To alleviate this problem, we transformed a conventional harvesting platform into a collaborative robot (co-robot) platform. As the co-robotic platform travels forward, it estimates the incoming fruit distribution using a vision system, it measures each worker's picking speed using instrumented picking bags, and controls the heights of hydraulic lifts that move workers up and down. The model-based control algorithm maximizes the machine's harvesting speed by changing the height at which each worker harvests as a response to incoming fruit load because it matches fruit-picking labor supply and demand. Simulation experiments with pre-recorded fruit distribution data validated the approach and provided efficiency gains under various conditions. Apple-harvesting experiments were also performed in a commercial orchard, where 2,307 kg of apples were picked: 1,045kg in variable-height zone 1 harvesting mode, and 1,262 kg in fixed zone harvesting mode, with workers at fixed heights that were set by the grower. Variable-height zone harvesting mode throughput was 327.6 kg/h vs. 298.8 kg/h for fixed zone harvesting mode at human-controlled platform moving speed, resulting in an improvement of 9.5%.

Advancement in artificial intelligence for on-farm fruit sorting and transportation

“…Workers’ picking speeds vary based on fruit distribution and the individual’s work pace. To address this issue, crop transport harvesting-aid robots have been developed to manage full and empty bins/trays as harvesting aids to increase the productivity, safety, and aesthetics of manual harvesting ( Fei and Vougioukas, 2021 ; Guevara et al., 2021 ). Since harvesting-aid robots are subject to the constraints of space between crop rows and the cooperation with pickers, maneuvering strategies are required to determine the transport path and operation to deliver full and empty bins in the field.…”

Design and performance evaluation of a novel end-effector with integrated gripper cum cutter for harvesting greenhouse produce

“…The stereo vision system which provides control of the actions to be performed by the robotic arm is attached to the end effector [28]. The modularity of robotic arms to perform different types of agricultural tasks has yielded encouraging outcomes [29]. Gripper for strawberry harvesting with measurements of maximum gripping force under the variety of picking techniques has been studied [30].…”

Integrating Function Allocation and Operational Event Sequence Diagrams to Support Human-Robot Coordination: Case Study of a Robotic Date Thinning System

“…The majority of the reports on agricultural technological developments detail the technical aspects of the invention (De-An et al, 2011; Hohimer et al, 2019). Fewer details are provided concerning the features associated with the human operator, such as how, when, and under what conditions the operator collaborates with the new agricultural system and what are the expected consequences of that collaboration (Fei & Vougioukas, 2021; Reina et al, 2016; Silwal et al, 2017). Some agricultural system studies have evaluated the implications of a limited number of ways to couple the human with the robotic system’s autonomy (Berenstein & Edan, 2012; Bergerman et al, 2015; Tkach et al, 2011); others explored the consequences of a particular user interface design on the operator’s situation awareness and performance in the agricultural work environment (Adamides et al, 2017; Anagnostis et al, 2021; Cullen et al, 2012; Scholtz et al, 2005).…”