In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target reprojection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.
Several IBVS @cite_8 @cite_18 @cite_17 and PBVS @cite_0 @cite_20 approaches has been applied to control aerial vehicles in the last decades. In those standard solutions, the controller uses the visual information as the main source for the target pose computation, taking into account where the target is re-projected into the image plane along the trajectory. A possible solution that usually mitigates such weakness is the kinematic limitation of the multirotor in terms of roll and pitch angles, but this penalizes the vehicle maneuverability. To this end, Ozawa @cite_6 present an approach that takes advantage of the rotational-dynamic of the vehicle, where a virtual spring penalizes large rotation with respect to a gravity aligned frame. Some recent approaches map the target features' dynamic into a "virtual image-plane" used to compensate the current roll and pitch angles, in order to keep them close to zero @cite_15 @cite_7 @cite_19 . Being the re-projection error obtained from the rotation-compensated frame, it is still possible that the target, due to significant rotations, completely leaves the camera field of view.
@cite_18 @cite_7 @cite_8 @cite_6 @cite_0 @cite_19 @cite_15 @cite_20 @cite_17
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A new image-based control strategy for visual servoing of a class of under-actuated rigid body systems is presented. The proposed control design applies to "eye-in-hand" systems where the camera is fixed to a rigid body with actuated dynamics. The control design is motivated by a theoretical analysis of the dynamic equations of motion of a rigid body and exploits passivity-like properties of these dynamics to derive a Lyapunov control algorithm using robust backstepping techniques. The proposed control is novel in considering the full dynamic system incorporating all degrees of freedom (albeit for a restricted class of dynamics) and in not requiring measurement of the relative depths of the observed image points. A motivating application is the stabilization of a scale model autonomous helicopter over a marked landing pad. This paper addresses the dynamics, control, planning, and visual servoing for micro aerial vehicles to perform high-speed aerial grasping tasks. We draw inspiration from agile, fast-moving birds, such as raptors, that detect, locate, and execute high-speed swoop maneuvers to capture prey. Since these grasping maneuvers are predominantly in the sagittal plane, we consider the planar system and present mathematical models and algorithms for motion planning and control, required to incorporate similar capabilities in quadrotors equipped with a monocular camera. In particular, we develop a dynamical model directly in the image space, show that this is a differentially-flat system with the image features serving as flat outputs, outline a method for generating trajectories directly in the image feature space, develop a geometric visual controller that considers the second order dynamics (in contrast to most visual servoing controllers that assume first order dynamics), and present validation of our methods through both simulations and experiments. 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. This paper presents an image-based visual servoing for controlling the position and orientation of a quadrotor using a fixed downward camera observing landmarks on the level ground. In the proposed method, the negative feedback to the image moments is used to control the vertical motion and rotation around the roll axis. On the other hand, the negative feedback cannot be used to control the horizontal motion due to under-actuation of a quadrotor. Thus, a novel control method is introduced to control the horizontal motion. Simulations are presented to validate the proposed method. We present control methods for an autonomous four-rotor helicopter, called a quadrotor, using visual feedback as the primary sensor. The vision system uses aground camera to estimate the pose (position and orientation) of the helicopter. Two methods of control are studied - one using a series of mode-based, feedback linearizing controllers, and the other using a backstepping-like control law. Various simulations of the model demonstrate the implementation of feedback linearization and the backstepping controllers. Finally,. we present initial flight experiments. where the helicopter is restricted to vertical and yaw motions. In this article, image-based visual servoing control of an underactuated unmanned aerial vehicle is considered for the three-dimensional translational motion. Taking into account the low quality of accelerometers’ data, the main objective of this article is to only use information of rate gyroscopes and a camera, as the sensor suite, in order to design an image-based visual servoing controller. Kinematics and dynamics of the unmanned aerial vehicle are expressed in terms of visual information, which make it possible to design dynamic image-based visual servoing controllers without using linear velocity information obtained from accelerometers. Image features are selected through perspective image moments of a flat target plane in which no geometric information is required, and therefore, the approach can be applied in unknown environments. Two output feedback controllers that deal with uncertainties in dynamics of the system related to the motion of the target and also unknown depth information of the image are proposed using a linear observer. Stability analysis guarantees that the errors of the system remain uniformly ultimately bounded during a tracking mission and converge to 0 when the target is stationary. Simulation results are presented to validate the designed controllers. In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor. We present the design and implementation of a vision-based feature tracking system for an autonomous helicopter. Visual sensing is used for estimating the position and velocity of features in the image plane (urban features like windows) in order to generate velocity references for the flight control. These visual-based references are then combined with GPS-positioning references to navigate towards these features and then track them. We present results from experimental flight trials, performed in two UAV systems and under different conditions, that show the feasibility and robustness of our approach. © 2006 Wiley Periodicals, Inc. An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight. The proposed control design addresses visual servo of 'eye-in-hand' type systems. The control of the position and orientation dynamics are decoupled using a visual error based on a spherical centroid data, along with estimation of the gravitational inertial direction. The error used compensates for the poor conditioning of the Jacobian matrix seen in earlier work in this area by introducing a non-homogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller is derived for the full dynamics of the system. Experimental results on an experimental UAV known as an X4-flyer made by the French Atomic Energy Commission (CEA) demonstrate the robustness and performances of the proposed control strategy.