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.

[1]  Phil Howlett,et al.  The Mekong—applications of value at risk (VaR) and conditional value at risk (CVaR) simulation to the benefits, costs and consequences of water resources development in a large river basin , 2007 .

[2]  Eduardo L. Pasiliao,et al.  Detecting resilient structures in stochastic networks: A two‐stage stochastic optimization approach , 2017, Networks.

[3]  Siddhartha S. Mehta,et al.  Optimal task allocation in multi-human multi-robot interaction , 2015, Optimization Letters.

[4]  Blake Hannaford,et al.  Telerobotics: Display, control, and communication problems , 1987, IEEE J. Robotics Autom..

[5]  Yew-Soon Ong,et al.  Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks , 2016, Ann. Oper. Res..

[6]  Keng Peng Tee,et al.  A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration , 2016, IEEE Transactions on Robotics.

[7]  Maja J. Mataric,et al.  Robot life-long task learning from human demonstrations: a Bayesian approach , 2017, Auton. Robots.

[8]  P. Krokhmal.  Higher moment coherent risk measures , 2007 .

[9]  Eduardo Moreno,et al.  Robust Planning for an Open-Pit Mining Problem under Ore-Grade Uncertainty , 2011, Electron. Notes Discret. Math..

[10]  Jingjing Jiang,et al.  Shared-Control for the Kinematic and the Dynamic Model of a Mobile Robot , 2016 .

[11]  Minoru Asada,et al.  Efficient human-robot collaboration: When should a robot take initiative? , 2017, Int. J. Robotics Res..

[12]  Eduardo L. Pasiliao,et al.  A Scenario Decomposition Algorithm for Stochastic Programming Problems with a Class of Downside Risk Measures , 2015, INFORMS J. Comput..

[13]  R. Rockafellar,et al.  Conditional Value-at-Risk for General Loss Distributions , 2001 .

[14]  Julie A. Shah,et al.  Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences , 2017, Int. J. Robotics Res..

[15]  Jake K. Aggarwal,et al.  BWIBots: A platform for bridging the gap between AI and human–robot interaction research , 2017, Int. J. Robotics Res..

[16]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[17]  Shahin Sirouspour,et al.  Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk , 2016, IEEE Transactions on Sustainable Energy.

[18]  Mohammad Reza Nikoo,et al.  A Conditional Value at Risk-Based Model for Planning Agricultural Water and Return Flow Allocation in River Systems , 2015, Water Resources Management.

[19]  Xiao-Ping Zhang,et al.  Real-Time Scheduling of Residential Appliances via Conditional Risk-at-Value , 2014, IEEE Transactions on Smart Grid.

[20]  Nisar R. Ahmed,et al.  Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration , 2013, IEEE Transactions on Robotics.