Robotics, Formal Verification, Validation, Behavior-Based
Artificial Intelligence and Robotics | Computer Engineering | Robotics
Abstract— A key challenge in the automatic verification of robot mission software, especially critical mission software, is to be able to effectively model the performance of a human operator and factor that into the formal performance guarantees for the mission. We present a novel approach to modelling the skill level of the operator and integrating it into automatic verification using a linear Gaussians model parameterized by experimental calibration. Our approach allows us to model different skill levels directly in terms of the behavior of the lumped, robot plus operator, system.
Using MissionLab and VIPARS (a behavior-based robot mission verification module), we present a comparison of our predicted performance guarantees for two missions in which a teleoperated quadrotor identifies a target for an autonomous ground robot to intercept: one mission in which the operator flies the quadrotor by line of sight to locate the target and one where the operator flies the quadrotor using its video feed. We demonstrate the effectiveness of our approach by comparing predicated performance to experimentally measured performance.
IEEE Int. Conf. on Systems, Man & Cybernetics, Banff Canada Oct. 2017.
Damian Lyons, Ron Arkin, Shu Jiang, Matthew O'Brien, Feng Tang and Peng Tang, "Formal performance Guarantees for an Approach to Human in the Loop Robot Missions.” IEEE Int. Conf. on Systems, Man & Cybernetics, Banff Canada Oct. 2017.
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