In this work a new algorithm is derived for the onboard calibration of three-axis strapdown magnetometers. The proposed calibration method is written in the sensor frame, and compensates for the combined effect of all linear time-invariant distortions, namely soft iron, hard iron, sensor nonorthogonality, and bias, among others. A maximum likelihood estimator (MLE) is formulated to iteratively find the optimal calibration parameters that best fit to the onboard sensor readings, without requiring external attitude references. It is shown that the proposed calibration technique is equivalent to the estimation of a rotation, scaling and translation transformation, and that the sensor alignment matrix is given by the solution of the orthogonal Procrustes problem. Good initial conditions for the iterative algorithm are obtained by a suboptimal batch least squares computation. Simulation and experimental results with low-cost sensors data are presented and discussed, supporting the application of the algorithm to autonomous vehicles and other robotic platforms.

The article of record as published may be found at http://dx.doi.org/10.2514/2.4229A new methodology is introduced for integrated design of guidance and control systems for autonomous vehicles (AVs). The methodology proposed borrows from the theory of gain-scheduled control and leads to an efficient procedure for the design of controllers for AVs to accurately track reference trajectories defined in an inertial reference frame. The paper illustrates the application of this procedure to the design of a tracking controller for the unmanned air vehicle Bluebird. The design phase is summarized, and the performance of the resulting controller is assessed in simulation using dynamic models of the vehicle and its sensor suite.Naval Air Systems Command under the Maritime Avionics Subsystems Technology PorgamNATO FellowshipNaval Air Systems Command under the Maritime Avionics Subsystems Technology PorgamNATO Fellowshi

This paper addresses the landing problem of a vertical take-off and landing vehicle, exemplified by a quadrotor, on a moving platform using image-based visual servo control. Observable features on a flat and textured target plane are exploited to derive a suitable control law. The target plane may be moving with bounded linear acceleration in any direction. For control purposes, the image of the centroid for a collection of landmarks is used as position measurement, whereas the translational optical flow is used as velocity measurement. The proposed control law guarantees convergence to the desired landing spot on the target plane, without estimating any parameter related to the unknown height, which is also guaranteed to remain strictly positive. Moreover, convergence is guaranteed even in the presence of bounded and possibly time-varying disturbances, resulting, for example, from the motion of the target plane, measurement errors, or wind-induced force disturbances. To improve performance, an estimator for unknown constant force disturbances is also included in the control law. Simulation and experimental results are provided to illustrate and assess the performance of the proposed controller.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

In this paper, a novel adaptive tracking control technique is developed for multiple-input-multiple-output (MIMO) nonlinear systems with model uncertainty and under output constraints occurring in a limited time interval (OCOLT). The OCOLT, which is a type of constraints occurring some time after (rather than from the beginning of) system operation and with limited duration, can be found in many practical systems and has not been effectively addressed in the literature up to now. By designing a new shift function and with the aid of barrier functions, the constrained system is transformed into an unconstrained one. A new disturbance observer then is designed to estimate unknown disturbances. The resultant control method is able to not only address the OCOLT, but also deal with the constraint-free output case and the infinite-time constrained one (i.e., constraints existing for all $t\geq 0$) without the need to revise the control structure. Finally, The effectiveness of the proposed technique is assessed in simulation.