Vidění pro roboty a autonomní systémy
We consider the task of active exploration of large subterranean
environments with a ground mobile robot.
Our goal is to autonomously explore a large unknown area and to obtain
an accurate coverage and localization of objects of interest
The exploration is constrained by the restricted operation time in
rescue scenarios, as well as a hard rough terrain.
To this end, we introduce a novel optimization strategy that respects
these constraints by maximizing the environment coverage by onboard
sensors while producing feasible trajectories with the help of a learned robot-terrain interaction model.
The approach is evaluated in diverse subterranean simulated
environments showing the viability of active exploration in challenging
In addition, we demonstrate that the local trajectory optimization
improves global coverage of an environment as well as the overall
object detection results.