Shared control for navigation and balance of a dynamically stable robot

Abstract

We developed a semi-autonomous control for a dynamically stable robot, Gyrover, by combining machine intelligence and human operating behaviors into a shared control environment. In this system, the entire control task is shared between the autonomous module and the human operator: the robot itself maintains local balancing, while the operator is responsible for the global navigation. The autonomous module consists of two unique and essential behaviors: lateral balancing and fall recovery. These behaviors are modeled by a machine learning algorithm. We developed a method enabling the system to make a reasonable decision in shared control, and addressed the implementation issues in the paper. Experiments demonstrated that this shared control scheme provides an efficient way to control a dynamically stable system, such as Gyrover.