3 accepted papers
We focus on planning minimum-length robot paths to cover environments using the robot's sensor or coverage (e.g. cleaning) tool. Many algorithms use the following framework: (i) compute a grid decomposition of the environment, (ii) partition the grid to be covered by non-overlapping coverage lines (…
This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynami
In this letter, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://