Autonomous navigation for UAVs managing motion and sensing uncertainty

TítuloAutonomous navigation for UAVs managing motion and sensing uncertainty
AutoresAdrián González-Sieira, Daniel Cores, Manuel Mucientes, Alberto Bugarín
TipoArtículo de revista
Fonte Robotics and autonomous systems, ELSEVIER SCIENCE BV, Vol. 126, pp. 103455 (1-12) , 2020.
RankProvisionally ranked Q1 in Computer Science Applications by SJR 2019
ISSN0921-8890
DOI10.1016/j.robot.2020.103455
AbstractWe present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the probability of collision from the predicted uncertainty. The motion planner relies on a graduated fidelity state lattice and a novel multi-resolution heuristic which adapt to the obstacles in the map. This allows managing the uncertainty at planning time and yet obtaining solutions fast enough to control the UAV in real time. Experimental results show the reliability and the efficiency of our approach in different real environments and with different motion models. Finally, we also report planning results for the reconstruction of 3D scenarios, showing that with our approach the UAV can obtain a precise 3D model autonomously.
Palabras chaveautonomous navigation, motion planning, motion uncertainty, UAVs, scene reconstruction