This paper proposes a model identification method and evaluation of a force model for multirotor UAVs. The model incorporates propellers' aerodynamics derived from momentum and blade element theories, as well as aerodynamics of the UAV's structure and actuation dynamics. A two-steps identification approach of the model parameters is proposed. The model is identified and evaluated from outdoor experiments with flight speeds exceeding 10m/s.

A solution to visuo-inertial filtering and estimation based on homography, angular velocity, and specific acceleration measurements is proposed. This corresponds to the typical situation of a mono-camera/IMU sensor facing a (locally) planar environment. By lifting the estimation state space to a higher-dimensionnal space, we show that the problem can be formulated as a linear estimation problem. This allows for the application of classical estimation techniques, e.g., Kalman filtering. Based on this linear formulation, we also determine explicitly the motion conditions that ensure uniform observability of the system, and we propose a simple linear Luenberger-like observer. A validation of the proposed solution based on real data is presented.

The paper concerns visuo-inertial filtering and estimation based on homography and angular velocity measurements, i.e. data obtained from a mono-camera/IMU sensor. We extend recently developed nonlinear filters on the special linear group of homographies to the estimation of scene parameters and velocity of the sensor. A validation of the proposed solution and a comparative evaluation based on real data is presented.

This paper addresses the person following problem for nonholonomic wheeled robots. Because of the robot's nonholonomy and the difficulty to estimate the person orientation, classical control laws used to address this problem induce strong limitations on the desired robot location with respect to the person. We propose a new nonlinear control law that allows for much more versatility in this following application. Simulation and experimental results performed in real scenarios verify the effectiveness of the proposed approach.

This article addresses the problem of direct vision-based robot control where the equilibrium state is defined via a reference image. Direct methods refer to intensity-based nonmetric techniques to perform that stabilization. Intensity-based strategies provide for higher accuracy, whereas not requiring any metric information improves their versatility. However, existing direct techniques either have a coupled error dynamics, or are designed for planar objects only. This paper proposes a new direct technique that decouples the translational motion from the rotational one for the general case of both planar and nonplanar targets under general translational and rotational displacements. Furthermore, for the important case of a fronto-parallel planar object, the proposed technique leads to a fully diagonal interaction matrix. The equilibrium state is made locally exponentially stable for all those cases. These improvements are theoretically proven and experimentally demonstrated using a 6-DoF robotic arm.

The paper addresses the control of Vertical Take Off and Landing (VTOL) Underactuated Autonomous Vehicles (UAVs) in hover flight, based on measurements provided by an on-board video camera and rate gyros. The objective is to stabilize the vehicle to the pose associated with a visual image of a planar target. By using the homography matrix computed from the camera measurements of the target, stabilizing feedback laws are derived. Explicit stability conditions on the control parameters are provided. It is shown that very good robustness and performance can be achieved without any apriori information on the visual target (like geometry, or orientation), by a proper tuning of the control parameters. Simulation results confirm the effectiveness of the approach.

This paper concerns the problem of position and attitude estimation based on the fusion of complementary sensors. Nonlinear observers are proposed in order to obtain high-quality and high-rate estimates from raw position/attitude data, and measurements obtained from an inertial measurement unit. Estimates are improved through the online identification of diverse measurement biases, and a novel method for con current position estimation and accelerometer bias estimation is presented. Almost global asymptotic convergence of the pose observer is proved based on a Lyapunov-like approach, and several implementation issues are addressed. Comparison with classical existing methods illustrate the relevance of the proposed approach.

This paper concerns the problem of pose estimation for an inertial-visual sensor. It is well known that IMU bias, and calibration errors between camera and IMU frames can impair the achievement of high-quality estimates through the fusion of visual and inertial data. The main contribution of this work is the design of new observers to estimate pose, IMU bias and camera-to-IMU rotation. The observers design relies on an extension of the so-called passive complementary filter on SO(3). Stability of the observers is established using Lyapunov functions under adequate observability conditions. Experimental results are presented to assess this approach.

A control approach for a class of underactuated vehicles with the objective of stabilizing reference trajectories either in velocity or position is proposed. The basic modeling assumption is that the vehicle is propulsed via a thrust force along a single body-fixed direction and that it has full torque actuation for attitude control (i.e. a typical actuation structure for aircrafts, vertical take-off and landing (VTOL) vehicles, submarines, etc.). Additional assumptions on the external forces applied to the vehicle are also introduced for the sake of control design and stability analyses. They are best satisfied for vehicles whose shape induces lift forces with limited amplitude and which are subjected to an external force field (e.g. gravity), as in the case of VTOL vehicles. The interactions of the vehicle with the surrounding fluid are often difficult to model precisely and they may significantly perturb its motion. Some novel nonlinear feedback control laws are proposed to compensate for modeling errors and perform robustly against such perturbations. Simulation results illustrating these properties on a realistic model of a VTOL vehicle are reported.

In the last decade, many vision-based robot controllers have been designed using Cartesian information encoded in the homography transformation that links two images of a planar object. For any approach, the performance of the closed-loop system depends on the quality of the homography estimates obtained. In this paper, we exploit the special linear Lie-group structure of the set of all homographies to develop a dynamic observer to estimate homographies online. The resulting estimates are effective and can be used to improve closed-loop response of several visual servoing algorithms.

The problem of stabilizing reference trajectories - also referred to as the trajectory tracking problem - for nonholonomic mobile robots is revisited. Theoretical difficulties and impossibilities that set inevitable limits to what is achievable with feedback control are surveyed, and properties of kinematic control models are recalled, with a focus on controllable driftless systems that are invariant on a Lie group. This geometric framework takes advantage of ubiquitous symmetry properties involved in the motion of mechanical bodies. The transverse function approach, a control design method developed by the authors for the past few years, is reviewed. A salient feature of this approach, which singles it out of the abundant literature devoted to the subject, is the obtention of feedback laws that unconditionally achieve the practical stabilization of arbitrary reference trajectories, including fixed points and nonadmissible trajectories. This property is complemented with novel results showing how the more common property of asymptotic stabilization of a large class of admissible trajectories can also be granted with this type of control. Application to unicycle-type and car-like vehicles is presented and illustrated via simulations. Complementary issues (transient maneuvers monitoring, extensions of the approach to systems that are not invariant on a Lie group, etc.) are also addressed with the concern of practicality.

The paper addresses the problem of controlling the posture of a nonholonomic mobile robot via sensor-based target tracking. The control technique here considered is adapted from the transverse function approach with sensory signals used to calculate an estimate of the relative pose of the robot with respect to the target. An analysis of stability and robustness w.r.t. pose estimation errors is presented and the overall control performance is tested via simulation and experimentation on a unicycle-type mobile robot equipped with a camera