Risk-averse Optimization for Improving Harvesting Efficiency of Autonomous Systems through Human Collaboration

Abstract

Autonomous systems operating in unstructured and complex agricultural environments are susceptible to errors leading to uncertain losses in the efficiency of the system. In robotic harvesting, these losses would translate into lower harvesting efficiency. To this end, it is desirable to improve harvesting efficiency of robotic systems through human collaboration. The added labor costs associated with human involvement could be a concern since the robotic harvesting systems are expected to reduce harvesting costs. Therefore, the objective of this work is to develop optimal human-robot collaboration policies that minimize the risk of economic losses by identifying the components of the system that need to be serviced by a human supervisor all while guaranteeing a desired level of financial return. The developed risk-averse optimization solution is verified in a simulated grove environment for Florida Valencia citrus.