150 research outputs found
Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints
Safe navigation of Micro Aerial Vehicles (MAVs) requires not only
obstacle-free flight paths according to a static environment map, but also the
perception of and reaction to previously unknown and dynamic objects. This
implies that the onboard sensors cover the current flight direction. Due to the
limited payload of MAVs, full sensor coverage of the environment has to be
traded off with flight time. Thus, often only a part of the environment is
covered.
We present a combined allocentric complete planning and trajectory
optimization approach taking these sensor visibility constraints into account.
The optimized trajectories yield flight paths within the apex angle of a
Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to
perceive obstacles in the flight direction. Furthermore, the optimized
trajectories take the flight dynamics into account and contain the velocities
and accelerations along the path.
We evaluate our approach with a DJI Matrice 600 MAV and in simulation
employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Montreal, Canada, May 201
Modeling, identification and navigation of autonomous air vehicles
The main interest of this work is autonomous navigation of autonomous air vehicles, specifically quadrotor helicopters (quadrocopters), and the focus is on convergence to a target destination with collision avoidance. The controller computes a collision-free path leading to the target position and is based on a navigation function approach and waypoints are followed exploiting PID controller
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
We address one of the main challenges towards autonomous quadrotor flight in
complex environments, which is flight through narrow gaps. While previous works
relied on off-board localization systems or on accurate prior knowledge of the
gap position and orientation, we rely solely on onboard sensing and computing
and estimate the full state by fusing gap detection from a single onboard
camera with an IMU. This problem is challenging for two reasons: (i) the
quadrotor pose uncertainty with respect to the gap increases quadratically with
the distance from the gap; (ii) the quadrotor has to actively control its
orientation towards the gap to enable state estimation (i.e., active vision).
We solve this problem by generating a trajectory that considers geometric,
dynamic, and perception constraints: during the approach maneuver, the
quadrotor always faces the gap to allow state estimation, while respecting the
vehicle dynamics; during the traverse through the gap, the distance of the
quadrotor to the edges of the gap is maximized. Furthermore, we replan the
trajectory during its execution to cope with the varying uncertainty of the
state estimate. We successfully evaluate and demonstrate the proposed approach
in many real experiments. To the best of our knowledge, this is the first work
that addresses and achieves autonomous, aggressive flight through narrow gaps
using only onboard sensing and computing and without prior knowledge of the
pose of the gap
Minimum-time trajectory generation for quadrotors in constrained environments
In this paper, we present a novel strategy to compute minimum-time
trajectories for quadrotors in constrained environments. In particular, we
consider the motion in a given flying region with obstacles and take into
account the physical limitations of the vehicle. Instead of approaching the
optimization problem in its standard time-parameterized formulation, the
proposed strategy is based on an appealing re-formulation. Transverse
coordinates, expressing the distance from a frame path, are used to
parameterise the vehicle position and a spatial parameter is used as
independent variable. This re-formulation allows us to (i) obtain a fixed
horizon problem and (ii) easily formulate (fairly complex) position
constraints. The effectiveness of the proposed strategy is proven by numerical
computations on two different illustrative scenarios. Moreover, the optimal
trajectory generated in the second scenario is experimentally executed with a
real nano-quadrotor in order to show its feasibility.Comment: arXiv admin note: text overlap with arXiv:1702.0427
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