Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Abstract

In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: (1) the oversimplification in target detection by using artificial markers and (2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this study, we upgrade the target detection in Fast‐Tracker to detect and localise a human target based on deep learning and non‐linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360° active vision on a customised gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast‐Tracker by incorporating an occlusion‐aware mechanism that generates observable tracking trajectories. Comprehensive real‐world tests confirm the proposed system's robustness and real‐time capability. Benchmark comparisons with Fast‐Tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks. The cover image is based on the Original Article Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression by Can Cui et al., https://doi.org/10.1049/csy2.12033.

Dynamic grasping of manipulator based on realtime smooth trajectory generation

“…The Covariant Optimization is utilized in Jeon et al 23 to consider the target motion prediction of collision, and the collision term needs much time to calculate. The polynomial regression to predict object motion and generates a corresponding trajectory through the constraint-free QP optimizer is adopted in Chen et al 24 Fast-Tracker 25,26 improves this method by using Bezier curves to represent the target prediction trajectory, and adding dynamic constraints and time-varying confidence in the optimization. We use this method to predict the movement of all objects.…”

“…We adopt the Be´zier curve B(t) to indicate the object predicted trajectory. 26 The method in Pan et al 26 maintains a queue Q = q 1 , q 2 , Á Á Á , q L ½ , where q i = p t i , t i f g. p t i is the instantaneous pose of the moving object, and t i is the corresponding timestamp. Queue length is L and t L is the current time.…”

“…The cost function minimizes the residual distance between the observation data and the prediction trajectory 26 :…”

“…The Object-oriented software for Quadratic Programming 37 is used to solve the optimization problem of the Be´zier curve that meets the velocity and acceleration constraints in Han et al 25 and Pan et al 26 Our complete motion predicted trajectory is shown as follows:…”

QP Chaser: Polynomial Trajectory Generation for Autonomous Aerial Tracking

“…[14] defines visible regions and uses them in spatialtemporal trajectory optimization. [15], [16] apply a concept of path topology to enhance the robustness of visibility as in [17]. However, the above works only think of the visibility of the center of a target, not the whole body of it; hence, partial occlusion may occur while chasing the target.…”