Learning-Based Shared Control Using Gaussian Processes for Obstacle Avoidance in Teleoperated Robots

Abstract

Physically inspired models of the stochastic nature of the human-robot-environment interaction are generally difficult to derive from first principles, thus alternative data-driven approaches are an attractive option. In this article, Gaussian process regression is used to model a safe stop maneuver for a teleoperated robot. In the proposed approach, a limited number of discrete experimental training data points are acquired to fit (or learn) a Gaussian process model, which is then used to predict the evolution of the process over a desired continuous range (or domain). A confidence measure for those predictions is used as a tuning parameter in a shared control algorithm, and it is demonstrated that it can be used to assist a human operator by providing (low-level) obstacle avoidance when they utilize the robot to carry out safety-critical tasks that involve remote navigation using the robot. The algorithm is personalized in the sense that it can be tuned to match the specific driving style of the person that is teleoperating the robot over a specific terrain. Experimental results demonstrate that with the proposed shared controller enabled, the human operator is able to more easily maneuver the robot in environments with (potentially dangerous) static obstacles, thus keeping the robot safe and preserving the original state of the surroundings. The future evolution of this work will be to apply this shared controller to mobile robots that are being deployed to inspect hazardous nuclear environments, ensuring that they operate with increased safety.