Functional Path Optimisation for Exploration in Continuous Occupancy Maps

摘要

The objective of autonomous exploration is to produce a consistent representation of the environment. It involves complex decision-making, selecting the trajectories a robot should take in order to minimise the overall uncertainty in the model. Essentially, exploration is a path optimisation procedure for finding trajectories that efficiently learn the environment. The difficulty lies in the dimensionality and shape of the search space which prohibits a closed-form solution to the general exploration problem, making autonomous exploration an active field of research. The plethora of exploration methods in the literature offer different strategies for relaxing this intractable problem, most commonly by reducing the search space dimensionality, for example by discretising the path.