Three-dimensional guidance and control for ground moving target tracking by a quadrotor

Abstract

This paper develops a three-dimensional guidance and control algorithm to ensure that a manoeuverable target is preserved by a quadrotor in a long-term tracking scenario. The proposed guidance approach determines the desired altitude of the quadrotor to adjust the field of view (FOV) to the union of two desired trusted and critical regions. The dimensions of the desired trusted region depend on the controller performance that is evaluated by the distance of the target from the center of the FOV. The critical region is a predefined margin around the trusted region that is defined by the operator based on the upper bounds of the quadrotor and target localisation errors. It also depends on the duration and magnitude of the temporal increase in the target velocity compared to the quadrotor velocity. A sufficient condition is provided for the minimum desired altitude of the quadrotor to ensure that the target is maintained in the FOV. Furthermore, a model predictive control (MPC) is employed to preserve the target at the center of the aerial image and the desired altitude determined by the guidance law. Also, the integrals of the position errors are used to achieve null steady-state errors in the presence of wind disturbances. The simulation results show the effectiveness of the proposed approach in preserving the manoeuverable target in the FOV in the presence of the wind, the uncertainty of the target and quadrotor localisation, accelerations estimation errors, and terrain altitude variation.

Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey

“…Monocular imaging systems are not capable to consider the object depth, even though this is compensated with methods like the one presented in [30], where a real-time object localization and tracking strategy from monocular image sequences is proposed, using a dynamic Kalman model to integrate object detection and tracking. Improving the tracking performance as well as avoiding manual initialization for tracking is proposed in [31]. On the other hand, [32] report a method that merges optical flow and background subtraction to detect and track multiple moving targets from a UAV; moreover, the authors assume that the background and the UAV have diverse motion patterns, and employ a Kalman filter to reduce the miss-detection and false alarms.…”

In-flight estimation of quadrotor mass and inertia using all-accelerometer

“…After a quadrotor is designed, c.f. (Domingues, 2009;Sepehri Movafegh et al, 2021), several parameters need to be identified to obtain approximates of their actual values. This identification is done usually off-line before even the quadrotor is deployed for the first time (Bresciani, 2008;Krznar et al, 2018).…”