This work addresses the problem of coupling vision-based navigation systems for Unmanned Aerial Vehicles (UAVs) with robust obstacle avoidance capabilities. The former is formulated by a maximization of the point of interest visibility, while the latter is modeled by ellipsoidal repulsive areas. The whole problem is transcribed into an Optimal Control Problem (OCP), and solved in a few milliseconds by leveraging state-of-the-art numerical optimization. The resulting trajectories are then well suited to achieve the specified goal location while avoiding obstacles by a safety margin and minimizing the probability to loose track with the target of interest. Combining this technique with a proper ellipsoid shaping (e.g. augmenting the shape with the obstacle velocity, or the obstacle detection uncertainties) results in a robust obstacle avoidance behaviour. We validate our approach within extensive simulated experiments demonstrating (i) capability to satisfy all the constraints, and (ii) the avoidance reactivity even in challenging situations. We release with this paper the open source implementation
Steering a robot to its desired state by using visual feedbacks obtained from one or more cameras (requirement 3) is formally defined as Visual Servoing (VS), with several applications within the UAV domain @cite_15 @cite_24 @cite_5 @cite_22 . Among the others, in Falanga @cite_22 , the authors address the flight through narrow gaps by proposing an active-vision approach and by relying only on onboard sensing and computing. The system is capable to provide an accurate trajectory while simultaneously estimating the UAV's position by detecting the gap in the camera images. Nevertheless, it might fail in presence of unmodeled obstacles along the path.
@cite_24 @cite_5 @cite_15 @cite_22
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In this paper we propose a new control method for quadrotor autonomous landing on a visual target without linear velocity measurements. Only onboard sensing is exploited, such that only the images of the landing pad from a down-looking camera, along with data from an Inertial Measurement Unit's gyro, are used. The control system consists of an image-based nonlinear observer that estimates online the linear velocity of the vehicle and a backstepping image-based controller that generates attitude, and thrust setpoints to the quadrotor autopilot. Both observer and controller share the same feedback information: spherical visual features. Therefore no further image elaboration is needed for the estimation. This, along with the fact that only simple computations on low- and constant-dimension arrays are involved, makes the proposed solution computationally cheap. Real-hardware experiments on a quadrotor are carried out to verify the validity of the proposed control system. This paper presents an adaptive image-based visual servoing (IBVS) integratedwith adaptive slidingmode control for a vision-based operation of a quadrotor unmanned aerial vehicle (UAV). For a seamless integration with underactuated quadrotor dynamics, roll and pitch channels are decoupled from the other channels using virtual features. This allows a simple and accurate algorithm for estimating depth information and successful application of the proposed guidance and control algorithm.By employing an adaptive gain in the IBVS controlmethod, the chance of image feature loss is reduced, and performance and stability of the vision-guided UAV control system are improved. The overall setup allows image features to be placed at the desired position in the image plane of a camera mounted on the quadrotor UAV. Stability of the IBVS systemwith the controller is proved using Lyapunov stability analysis. Performance of the overall approach is validated by numerical simulation, vision integrated hardware-inthe-loop simulation, and experiments. The results confirm that the target image is successfully placed at the desired position of the image plane and the quadrotor state variables are properly regulated, showing robustness in the presence of sensor noise, parametric uncertainty, and vibration from motors. The motivation of this research is to show that visual based object tracking and following is reliable using a cheap GPS-denied multirotor platform such as the AR Drone 2.0. Our architecture allows the user to specify an object in the image that the robot has to follow from an approximate constant distance. At the current stage of our development, in the event of image tracking loss the system starts to hover and waits for the image tracking recovery or second detection, which requires the usage of odometry measurements for self stabilization. During the following task, our software utilizes the forward-facing camera images and part of the IMU data to calculate the references for the four on-board low-level control loops. To obtain a stronger wind disturbance rejection and an improved navigation performance, a yaw heading reference based on the IMU data is internally kept and updated by our control algorithm. We validate the architecture using an AR Drone 2.0 and the OpenTLD tracker in outdoor suburban areas. The experimental tests have shown robustness against wind perturbations, target occlusion and illumination changes, and the system's capability to track a great variety of objects present on suburban areas, for instance: walking or running people, windows, AC machines, static and moving cars and plants. 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 in the world reference frame, 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, achieving a success rate of 80 and gap orientations up to 45°. 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.