An integrated localization-navigation scheme for distance-based docking of UAVs

In this paper we study the distance-based docking problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously achieve the relative localization and navigation tasks for discrete-time integrators under bounded velocity: a nonlinear adaptive estimation scheme to estimate the relative position to the landmark, and a delicate control scheme to ensure both the convergence of the estimation and the asymptotic docking at the given landmark. A rigorous proof of convergence is provided by invoking the discrete-time LaSalle's invariance principle, and we also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a GPS-less environment

Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem

Aimed at solving the optimal control problem for nonlinear systems with unsymmetrical input constraints, we present an online adaptive approach for partially unknown control systems/dynamics. The designed algorithm converges online to the optimal control solution without the knowledge of the internal system dynamics. The optimality of the obtained control policy and the stability for the closed-loop dynamic optimality are proved theoretically. The proposed method greatly relaxes the assumption on the form of the internal dynamics and input constraints in previous works. Besides, the control design framework proposed in this paper offers a new approach to solve the optimal circumnavigation problem involving a moving target for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our method is compared with that of the existing circumnavigation control law in a numerical simulation and the simulation results validate the effectiveness of our algorithm

Target capture and station keeping of fixed speed vehicles without self-location information

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.ejcon.2018.06.003 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Target capture and station keeping problems for an autonomous vehicle agent have been studied in the literature for the cases where the position of the agent can be measured. Station keeping refers to moving the agent to a target whose distances are predefined from a set of beacons that can be stations or other agents. Here we study the target capture and station keeping problems for a nonholonomic vehicle agent that does not know its location and can measure only distances to the target (to the beacons for station keeping). This sensing limitation corresponds to consideration of unavailability of GPS and odometry in practical UAV settings. For each of the target capture and station keeping problems, we propose a control algorithm that uses only agent-target (agent-beacon for station keeping) range and range rate information. We show the stability and convergence properties of our control algorithms. We verified the performance of our control algorithms by simulations and real time experiments on a ground robot. Our algorithms captured the target in finite time in the experiments. Therefore, our algorithms are efficient in scenarios where GPS is unavailable or target identification by vision algorithms is unreliable but continuous agent-target range measurements are available.King Abdullah University of Science and Technolog