Bayesian optimisation for active perception and smooth navigation

Abstract

A key challenge for long-term autonomy is to enable a robot to automatically model properties of the environment while actively searching for better decisions to accomplish its task. This amounts to the problem of exploration-exploitation in the context of active perception. This paper addresses active perception and presents a technique to incrementally model the roughness of the terrain a robot navigates on while actively searching for waypoints that reduce the overall vibration experienced during travel. The approach employs Gaussian processes in conjunction with Bayesian optimisation for decision making. The algorithms are executed in real-time on the robot while it explores the environment. We present experiments with an outdoor vehicle navigating over several types of terrains demonstrating the properties and effectiveness of the approach.