188 research outputs found
Tight Collision Probability for UAV Motion Planning in Uncertain Environment
Operating unmanned aerial vehicles (UAVs) in complex environments that
feature dynamic obstacles and external disturbances poses significant
challenges, primarily due to the inherent uncertainty in such scenarios.
Additionally, inaccurate robot localization and modeling errors further
exacerbate these challenges. Recent research on UAV motion planning in static
environments has been unable to cope with the rapidly changing surroundings,
resulting in trajectories that may not be feasible. Moreover, previous
approaches that have addressed dynamic obstacles or external disturbances in
isolation are insufficient to handle the complexities of such environments.
This paper proposes a reliable motion planning framework for UAVs, integrating
various uncertainties into a chance constraint that characterizes the
uncertainty in a probabilistic manner. The chance constraint provides a
probabilistic safety certificate by calculating the collision probability
between the robot's Gaussian-distributed forward reachable set and states of
obstacles. To reduce the conservatism of the planned trajectory, we propose a
tight upper bound of the collision probability and evaluate it both exactly and
approximately. The approximated solution is used to generate motion primitives
as a reference trajectory, while the exact solution is leveraged to iteratively
optimize the trajectory for better results. Our method is thoroughly tested in
simulation and real-world experiments, verifying its reliability and
effectiveness in uncertain environments.Comment: Paper Accepted by IROS 202
- …