In this letter, we investigate the integration of a Dynamic Vision Sensor (DVS) into an Unmanned Aerial Vehicle (UAV) with a cable-suspended load in order to achieve a robust and fast estimation of the cable's state during the transportation of the load. Based on the advantageous properties of event cameras, our ultimate goal is to design a computationally lightweight event processing method that persistently identifies the cable and estimates its complete state – required for any controller with feedback of the cable's state – within a much shorter time period compared to frame-based algorithms. Using a point cloud representation for the incoming event streams, the proposed method achieves the fast detection of the cable while the respective measurements are afterward fitted to a Bézier curve in order to approximate both the cable angle and angular velocity. Our method is initially validated in an indoor environment, where ground truth is available from a motion capture system, and is subsequently deployed in an outdoor one in order to evaluate its robustness against noise. Throughout the outdoor experiment, the feedback provided by the DVS is incorporated into a Nonlinear Model Predictive Control (NMPC) scheme which drives an octorotor towards reference setpoint positions while minimizing the cable angular motion.

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.

In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV.

In this work, we leverage Gaussian Processes (GPs) and present a learning-based control scheme for the transportation of cable-suspended loads with multirotor Unmanned Aerial Vehicles (UAVs). Our ultimate goal is to approximate the model discrepancies that exist between the actual and nominal system dynamics. Towards this direction, weighted and sparse Gaussian Process (GP) regression is exploited so as to approximate online the model errors and guarantee real-time performance while also ensuring adaptability to the conditions prevailing in the outdoor environment where the UAV is deployed. The learned model errors are fed into a nonlinear Model Predictive Controller (NMPC), formulated for the corrected system dynamics, which achieves the transportation of the UAV towards reference positions with simultaneous minimization of the cable angular motion, regardless of the outdoor conditions and the existence of external disturbances, primarily stemming from the unknown wind. The proposed scheme is validated through simulations and real-world experiments with an octorotor, demonstrating an $80\%$ reduction in the steady-state position error under 4 Beaufort wind conditions compared to the nominal NMPC.