Balancing Robotic Teleoperation and Autonomy in a Complex and Dynamic Environment

Abstract

While Artificial Intelligence has been working to produce truly autonomous problem solving agents for many years, the current abilities of such agents are extremely limited. In highly complex, dynamic situations such as disaster rescue, today's agents simply do not have the ability to perform successfully on their own: the environment is di#cult to traverse and even to sense accurately, time is a significant factor, and the dynamic and unpredictable nature of the environment tends to preclude the ability to produce extensive plans for future activity. Because of these and other limitations, robotic agents for environments such as disaster rescue rely strongly on human teleoperation. This too has its limitations: humans become fatigued rapidly, su#er from cognitive overload when they obtain too much sensory information in a short time, and have di#culties in constructing a mental image of the space around a robot given information from its senses (situational awareness).