When Human Visual Performance Is Imperfect—How to Optimize the Collaboration Between One Human Operator and Multiple Field Robots

Abstract

In this chapter, we consider a robotic field exploration and classification task where the field robots have a limited communication with a remote human operator, and also have constrained motion energy budgets. We then extend our previously proposed paradigm for human–robot collaboration (Cai and Mostofi, Proceedings of the American control conference, pp 440–446, 2015 [4]), (Cai and Mostofi, Proceedings of Robotics: Science and Systems, 2016 [5]) to the case of multiple robots. In this paradigm, the robots predict human visual performance , which is not necessarily perfect, and optimize seeking help from humans accordingly (Cai and Mostofi, Proceedings of the American control conference, pp 440–446, 2015 [4]), (Cai and Mostofi, Proceedings of Robotics: Science and Systems, 2016 [5]. More specifically, given a probabilistic model of human visual performance from (Cai and Mostofi, Proceedings of the American control conference, pp 440–446, 2015 [4]), in this chapter we show how multiple robots can properly optimize motion, sensing, and seeking help. We mathematically and numerically analyze the properties of robots’ optimum decisions, in terms of when to ask humans for help, when to rely on their own judgment and when to gather more information from the field. Our theoretical results shed light on the properties of the optimum solution. Moreover, simulation results demonstrate the efficacy of our proposed approach and confirm that it can save resources considerably.