Nonlinear Visual Control of Unmanned Aerial Vehicles in GPS-Denied Environments

Abstract

In this paper, we propose a nonlinear controller that stabilizes unmanned aerial vehicles in GPS-denied environments with respect to visual targets by using only onboard sensing. The translational velocity of the vehicle is estimated online with a nonlinear observer, which exploits spherical visual features as the main source of information. With the proposed solution, only four visual features have shown to be enough for the observer to operate in a real scenario. In addition, the observer is computationally light with constant numerical complexity, involving small-dimension matrices. The observer output is then exploited in a nonlinear controller designed with an integral backstepping approach, thus yielding a novel robust control system. By means of Lyapunov analysis, the stability of the closed-loop system is proved. Extensive simulation and experimental tests with a quadrotor are carried out to verify the validity and robustness of the proposed approach. The control system runs fully onboard on a standard processor, and only a low-cost sensing suite is employed. Tracking of a target whose speed exceeds 2 m/s is also considered in the real-hardware experiments.Index Terms-Image-based visual servoing, nonlinear controller, nonlinear observer, unmanned aerial vehicle (UAV), velocity estimation.

Vision-Based Reactive Planning for Aggressive Target Tracking While Avoiding Collisions and Occlusions

“…1. An optimal trajectory for a quadrotor equipped with a camera is generated for reaching a minimum distance with a target (blue sphere) while avoiding collisions with spherical obstacles (inflated dark spheres) and occlusions of the target from the obstacles (red spheres) [2], [3] or [4] for quadrotors), the underlying assumptions fail for high speed manoeuvres and in any case, do not take into account possible loss of visibility or occlusions.…”

A Self-contained Teleoperated Quadrotor: On-Board State-Estimation and Indoor Obstacle Avoidance

“…Autonomous obstacle avoidance, especially when operating in tight and unstructured GPS-denied environments, can enhance visual inspection or search and rescue missions. Thus allowing for operation closer to objects of interest, and in more cluttered environments, leaving the operator free to focus on higher level goals [3], [4], [5]. Depending on the objective and the amount of a priori knowledge of the system, we can distinguish several formulations of the navigation problem.…”

“…Such an approach, usually based on inertial measurement unit (IMU) and optical flow integration ( [4], [12]), requires fewer resources and quite often can be executed on an embedded microcontroller [16]. Although the more resource-intensive methods such as visual odometry ( [5], [8]) or visual-inertial odometry ( [14], [13]) can provide low drift position estimates they also require higher computational power or even dedicated hardware.…”

A direct visual servoing‐based framework for the 2016 IROS Autonomous Drone Racing Challenge

“…[2][3][4][5][6][7][8][9] However, such delicate and expensive systems cannot be assumed always available in real world, to deploy UAVs into various real-world applications, one needs to employ a self-contained navigation method. Among a few solutions, the vision-based navigation is quite promising: indeed, a variety of vision-based approaches ranging from target recognition to visual servoing [10][11][12][13] have been successfully applied to UAVs. As the hardware for onboard vision system becomes more powerful yet lighter, self-contained vision-based navigation has become a viable solution for precision indoor navigation.…”