KEYWORDS: Micro unmanned aerial vehicles, Cameras, Monte Carlo methods, Stereo vision systems, Device simulation, Space operations, Sensors, 3D vision, Stereoscopic cameras, Navigation systems
We introduce a new approach for on-board autonomous obstacle avoidance for micro air vehicles flying outdoors in close proximity to structure. Our approach uses inverse-range, polar-perspective stereo-disparity maps for obstacle detection and representation, and deploys a closed-loop RRT planner that considers flight dynamics for trajectory generation. While motion planning is executed in 3D space, we reduce collision checking to a fast z-buffer-like operation in disparity space, which allows for significant speed-up compared to full 3d methods. Evaluations in simulation illustrate the robustness of our approach, whereas real world flights under tree canopy demonstrate the potential of the approach.
This paper considers a landing problem for an MAV that uses only a monocular camera for guidance. Although this
sensor cannot measure the absolute distance to the target, by using optical flow algorithms, time-to-collision to the target
is obtained. Existing work has applied a simple proportional feedback control to simple dynamics and demonstrated its
potential. However, due to the singularity in the time-to-collision measurement around the target, this feedback could
require an infinite control action. This paper extends the approach into nonlinear dynamics. In particular, we explicitly
consider the saturation of the actuator and include the effect of the aerial drag. It is shown that the convergence to the
target is guaranteed from a set of initial conditions, and the boundaries of such initial conditions in the state space are
numerically obtained. The paper then introduces parametric uncertainties in the vehicle model and in the time-to-collision
measurements. Using an argument similar to the nominal case, the robust convergence to the target is proven, but the region
of attraction is shown to shrink due to the existence of uncertainties. The numerical simulation validates these theoretical
results.
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