In this work, we present a nonlinear Model Predictive Control (NMPC) scheme for tracking a ground target using a multirotor with a cable-suspended load. The NMPC framework relies on the dynamic model of the UAV with the suspended load and, hence, an estimate of the load state is obtained by fusing the measurements of a downward-facing camera and a load cell with an Unscented Kalman Filter (UKF). Additionally, since the NMPC relies on the future behavior of the system, the trajectory of the ground target throughout the predicted time horizon of the NMPC, is required. Towards this direction, Bézier curves are employed in order to predict the future trajectory of the target, which moves in an arbitrary way. The ultimate goal of the proposed framework is to release the suspended load to the ground target and, consequently, a condition is checked at each time instant that triggers the opening of a gripper, located at the lower edge of the cable. The performance of the proposed control scheme is experimentally validated using an octorotor.

In this paper, an Event-triggered Image-based Visual Servoing Nonlinear Model Predictive Controller (ET-IBVS-NMPC) for multirotor aerial vehicles is presented. The proposed scheme is developed for the autonomous surveillance of contour-based areas with different characteristics (e.g. forest paths, coastlines, road pavements). For this purpose, an appropriately trained Deep Neural Network (DNN) is employed for the accurate detection of the contours. In an effort to reduce the remarkably large computational cost required by an IBVS-NMPC algorithm, a triggering condition is designed to define when the Optimal Control Problem (OCP) should be resolved and new control inputs will be calculated. Between two successive triggering instants, the control input trajectory is applied to the robot in an open-loop fashion, which means that no control input computations are required. As a result, the system's computing effort and energy consumption are lowered, while its autonomy and flight duration are increased. The visibility and input constraints, as well as the external disturbances, are all taken into account throughout the control design. The efficacy of the proposed strategy is demonstrated through a series of real-time experiments using a quadrotor and an octorotor both equipped with a monocular downward looking camera.

This paper addresses the problem of water sampling by using a multirotor UAV with a cable-suspended mechanism. In order to ensure the safe execution of the sampling procedure and the stabilization of the vehicle, the disturbances, induced by the water flow and transferred through the cable, have to be identified. Specifically, an estimate of the disturbances is extracted by integrating a depth sensor, a load cell, an ultrasonic sensor and a downward-looking camera into the UAV's sensor suite and fusing the respective measurements. Gaussian Processes are afterwards employed so as to learn the uncertain disturbances in real time and in a non-parametric manner. The predicted disturbances are incorporated into a geometric control scheme which is capable of stabilizing the UAV above the desired sampling position while compensating for the aforementioned disturbances. The performance of the proposed control strategy is demonstrated through both simulation and experimental results.