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1. 【Paper】2015_El H_Decentralized Control Architecture for UAV-UGV Cooperation
2. 【Paper】2015_El H_UAV-UGV Cooperation For Objects Transportation In An Industrial Area
3. 【Paper】2013_Double Exponential Smoothing for Predictive Vision Based Target Tracking

1 Introduction

1.1 Motivation and related works

Air-Ground Cooperation (AGC)
Unmanned Aerial Vehicles (UAVs)
Unmanned Ground Vehicles (UGVs)

1.2 Contribution and orgnization

The first contribution of our work consists in providing a real-time navigation scheme.

The second contribution concerns the visual-based formation.

2 Problem Statement

3 Architecture configuration

3.1 Hardware configuration

3.2 Overall architecture

3.3 First layer: Leader-Followers

we consider the following errors $e_{XF}$ and $e_{YF}$ corresponding to the differences between the initial and the tracking state:
$$\begin{gathered} e_{XF}=z\cdot \cos (\theta )-z_0\cdot \cos ({\theta }_0) \\ {e}_{YF}=z\cdot \sin (\theta )-z_0\cdot \sin ({\theta }_0) \end{gathered}$$

the time derivatives of $e_{XF}$ and $e_{YF}$ :
$$\begin{gathered} {\dot{e}}_{XF}=\dot{z}\cdot \cos (\theta )-z\cdot \dot{\theta }\cdot \sin (\theta ) \\ {e}_{YF}=\dot{z}\cdot \sin (\theta )-z\cdot \dot{\theta }\cdot \cos (\theta ) \end{gathered}$$

$$\begin{gathered} {\dot{e}}_{XF}=T{V}_{XF}-u \\ {\dot{e}}_{YF}=T{V}_{YF}-r\cdot L \end{gathered}$$

$$\begin{gathered} {\dot{e}}_{XF}=-K\cdot e_{XF} \\ {\dot{e}}_{YF}=-K\cdot e_{YF} \end{gathered}$$

$$\begin{gathered} T{V}_{XF}-u=-K\cdot (z\cdot \cos (\theta )-z_0\cdot \cos ({\theta }_0)) \\ T{V}_{YF}-r\cdot L=-K\cdot (z\cdot \sin (\theta )-z_0\cdot \sin ({\theta }_0)) \end{gathered}$$

Since the target’s velocities ( $T{V}_{XF},T{V}_{YF}$) are unknown, we propose the following non-linear kinematic controller:
$$\begin{gathered} u=K\cdot (z\cdot \cos (\theta )-z_0\cdot \cos ({\theta }_0)) \\ r=\frac{K\cdot (z\cdot \sin (\theta )-z_0\cdot \sin ({\theta }_0))}{L} \end{gathered}$$

Lyapunov candidate function:
$$\begin{gathered} V=\frac{e_{XF}^2+e_{YF}^2}{2}(V>0) \\ \dot{V}=e_{XF}\cdot {\dot{e}}_{XF}+e_{YF}\cdot {\dot{e}}_{YF} \end{gathered}$$

3.4 Second layer: Drone-Leader

4 Results

4.1 Simulation results

4.2 Experimental results

领航者轨迹仿真

设置初始位置为 $0$，初始角度也为 $0$。

跟随者

5 Conclusion and future work

Ref

Joseph J LaViola Jr. “an experiment comparing double exponential smoothing and kalman filter-based predictive tracking algorithms”. In Virtual Reality, 2003. Proceedings. IEEE, pages 283–284. IEEE, 2003.

最后

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