Abstract
This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.
Highlights
Unmanned aerial vehicles (UAVs) have received growing interest, due to their advantages of vertical take off and landing, rapid maneuverability, and low cost
This paper presents a simple vision-based strategy to track an uncooperative target which is newly discovered by the UAV
We have developed a vision-based control scheme of a quadrotor for target tracking in the absence of location information and geometric features of the target
Summary
Unmanned aerial vehicles (UAVs) have received growing interest, due to their advantages of vertical take off and landing, rapid maneuverability, and low cost. With improvements in sensing devices, batteries, materials, and other technologies, UAVs have sufficient payload and flight endurance, supporting many applications such as transportation, real-time monitoring, search and rescue, and security and surveillance [1,2,3,4]. There have been a variety of studies related to missions using autonomous hovering and tracking technologies [5,6,7]. In [5], a finite-time controller was proposed to drive a quadrotor hovering above a target with a limited duration. In [6], a novel fuzzy PID-type iterative learning control was developed for trajectory tracking of a quadrotor under the effects of external disturbances and uncertain factors of the system. The problem of energy-efficient path planning and simultaneously anticipating disturbances has been addressed in [7]
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