# Event-Triggered Intervention Framework for UAV-UGV Coordination Systems

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## Abstract

**:**

## 1. Introduction

## 2. Preliminary

## 3. Framework Design of Air-Ground Coordination Systems

#### 3.1. Task Planning Layer Task Design

#### 3.1.1. UGV Basic Task Function Design

#### UGV Move-to-Target Task Function Design

#### UGV Obstacles-Avoidance Task Function Design

#### 3.1.2. UAV Basic Task Function Design

#### UAV Formation Task Function Design

#### UAV Obstacles-Avoidance Task Function Design

#### 3.1.3. Composite Task Function Design

- (1)
- Assume that b = r is the lowest priority, and b = 1 is the top priority. Here, ${m}_{b}$ > ${m}_{a}$ implies that ${m}_{b}$ is the index of a lower priority than ${m}_{a}$; a task of priority ${m}_{b}$ may not disturb another task of priority ${m}_{a}$. The lower-priority tasks are executed in the null space of all higher priority tasks.
- (2)
- The mappings from the velocities to the task velocities are captured by the task Jacobian matrix ${J}_{b}\in {\mathbb{R}}^{{m}_{b}\times n}$, $\forall 1\le b\le r$.
- (3)
- The dimension ${m}_{r}$ of the lowest level task may be greater than $n-{\sum}_{b=1}^{r-1}{m}_{b}$ so that n is ensured to be greater than the total dimension of all tasks.

#### 3.2. UAV Intervention Task Design

**Assumption**

**1.**

#### 3.3. Decision-Making Layer Design

## 4. Optimization Layer Design Based on MPC

#### 4.1. Optimal Control Formulation

#### 4.2. Real-Time Model Predictive Cotrol Algorithm

## 5. Simulation

#### 5.1. Case A

#### 5.2. Case B

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Oh, K.K.; Park, M.C.; Ahn, H.S. A survey of multi-agent formation control. Automatica
**2015**, 53, 424–440. [Google Scholar] [CrossRef] - Lacroix, S.; Le Besnerais, G. Issues in cooperative air/ground robotic systems. In Robotics Research; Springer: Berlin/Heidelberg, Germany, 2010; pp. 421–432. [Google Scholar]
- Chen, J.; Zhang, X.; Xin, B.; Fang, H. Coordination between unmanned aerial and ground vehicles: A taxonomy and optimization perspective. IEEE Trans. Cybern.
**2015**, 46, 959–972. [Google Scholar] [CrossRef] [PubMed] - Qin, H.; Meng, Z.; Meng, W.; Chen, X.; Sun, H.; Lin, F.; Ang, M.H. Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Trans. Veh. Technol.
**2019**, 68, 1339–1350. [Google Scholar] [CrossRef] - Liu, Y.; Luo, Z.; Liu, Z.; Shi, J.; Cheng, G. Cooperative routing problem for ground vehicle and unmanned aerial vehicle: The application on intelligence, surveillance, and reconnaissance missions. IEEE Access
**2019**, 7, 63504–63518. [Google Scholar] [CrossRef] - Li, J.; Deng, G.; Luo, C.; Lin, Q.; Yan, Q.; Ming, Z. A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Trans. Veh. Technol.
**2016**, 65, 9585–9596. [Google Scholar] [CrossRef] - Tokekar, P.; Vander Hook, J.; Mulla, D.; Isler, V. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans. Robot.
**2016**, 32, 1498–1511. [Google Scholar] [CrossRef] - Ding, Y.; Xin, B.; Chen, J. A Review of Recent Advances in Coordination Between Unmanned Aerial and Ground Vehicles. Unmanned Syst.
**2021**, 9, 97–117. [Google Scholar] [CrossRef] - Rodriguez-Ramos, A.; Sampedro, C.; Bavle, H.; De La Puente, P.; Campoy, P. A deep reinforcement learning strategy for UAV autonomous landing on a moving platform. J. Intell. Robot. Syst.
**2019**, 93, 351–366. [Google Scholar] [CrossRef] - Jung, S.; Cho, H.; Kim, D.; Kim, K.; Han, J.I.; Myung, H. Development of algal bloom removal system using unmanned aerial vehicle and surface vehicle. IEEE Access
**2017**, 5, 22166–22176. [Google Scholar] [CrossRef] - Aranda, M.; ópez-Nicolás, G.; Sagüés, C.; Mezouar, Y. Formation control of mobile robots using multiple aerial cameras. IEEE Trans. Robot.
**2015**, 31, 1064–1071. [Google Scholar] [CrossRef] - Santana, L.V.; Brandão, A.S.; Sarcinelli-Filho, M. Heterogeneous leader-follower formation based on kinematic models. In Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA, 7–10 June 2016; pp. 342–346. [Google Scholar]
- Stentz, T.; Kelly, A.; Herman, H.; Rander, P.; Amidi, O.; Mandelbaum, R. Integrated Air/Ground Vehicle System for Semi-Autonomous Off-Road Navigation. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2002. [Google Scholar]
- Peterson, J.; Chaudhry, H.; Abdelatty, K.; Bird, J.; Kochersberger, K. Online aerial terrain mapping for ground robot navigation. Sensors
**2018**, 18, 630. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mathews, N.; Christensen, A.L.; Stranieri, A.; Scheidler, A.; Dorigo, M. Supervised morphogenesis: Exploiting morphological flexibility of self-assembling multirobot systems through cooperation with aerial robots. Robot. Auton. Syst.
**2019**, 112, 154–167. [Google Scholar] [CrossRef] [Green Version] - Marino, A. A Null-Space-based Behavioral Approach to Multi-Robot Patrolling. Ph.D. Thesis, Universita degli Studi della Basilicata, Potenza, Italy, 2004. [Google Scholar]
- Yao, P.; Wei, Y.; Zhao, Z. Null-space-based modulated reference trajectory generator for multi-robots formation in obstacle environment. ISA Trans.
**2021**. [Google Scholar] [CrossRef] [PubMed] - Moreira, M.S.M.; Brandão, A.S.; Sarcinelli-Filho, M. Null space based formation control for a uav landing on a ugv. In Proceedings of the 2019 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 11–14 June 2019; pp. 1389–1397. [Google Scholar]
- Bacheti, V.P.; Brandão, A.S.; Sarcinelli-Filho, M. Path-following by a UGV-UAV Formation Based on Null Space. In Proceedings of the 2021 14th IEEE International Conference on Industry Applications (INDUSCON), São Paulo, Brazil, 15–18 August 2021; pp. 1266–1273. [Google Scholar]
- Huang, J.; Wu, W.; Zhang, Z.; Chen, Y. A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems. IEEE Access
**2020**, 8, 197853–197862. [Google Scholar] [CrossRef] - Arrichiello, F. Coordination Control of Multiple Mobile Robots. Ph.D. Thesis, Universita Degli Studi Di Cassino, Cassino, Italy, 2006. [Google Scholar]
- Bogacz, R.; Brown, E.; Moehlis, J.; Holmes, P.; Cohen, J.D. The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev.
**2006**, 113, 700. [Google Scholar] [CrossRef] [PubMed] - Huang, J.; Zhou, N.; Cao, M. Adaptive fuzzy behavioral control of second-order autonomous agents with prioritized missions: Theory and experiments. IEEE Trans. Ind. Electron.
**2019**, 66, 9612–9622. [Google Scholar] [CrossRef] - Nirawana, I.W.S.; Aryanto, K.Y.E.; Indrawan, G. Mobile Robot Based Autonomous Selection of Fuzzy-PID Behavior and Visual Odometry for Navigation and Avoiding Barriers in the Plant Environment. In Proceedings of the 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, 26–27 November 2018; pp. 234–239. [Google Scholar]
- Chen, L.; Wu, M.; Zhou, M.; She, J.; Dong, F.; Hirota, K. Information-driven multirobot behavior adaptation to emotional intention in human–robot interaction. IEEE Trans. Cogn. Dev. Syst.
**2017**, 10, 647–658. [Google Scholar] [CrossRef] - Nie, M.; Luo, D.; Liu, T.; Wu, X. Action Selection Based on Prediction for Robot Planning. In Proceedings of the 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Oslo, Norway, 19–22 August 2019; pp. 201–206. [Google Scholar]
- Chen, Y.; Zhang, Z.; Huang, J. Dynamic task priority planning for null-space behavioral control of multi-agent systems. IEEE Access
**2020**, 8, 149643–149651. [Google Scholar] [CrossRef] - Sager, S. Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control. J. Process Control
**2009**, 19, 1238–1247. [Google Scholar] [CrossRef] [Green Version] - Bock, H.G.; Plitt, K.J. A multiple shooting algorithm for direct solution of optimal control problems. IFAC Proc. Vol.
**1984**, 17, 1603–1608. [Google Scholar] [CrossRef]

**Figure 1.**Task velocity composition in the NSBC framework. The velocity ${v}^{(i+1)}$ is projected into the null space of the higher priority task and added to ${v}_{d,i}$. Finally, ${v}_{d,i}$ and ${N}_{i}\times {v}^{(i+1)}$ get the composite task speed ${v}^{i}$ through the vector sum.

**Figure 2.**Framework design of air-ground cooperative system. It is mainly composed of three layers. (1) Task planning layer: is responsible for task design based on NSBC and resolution of task conflicts (2) Decision-making layer: is mainly responsible for judging the timing of intervention. (3) Optimization layer: optimizes the decision-acceptance problem for UGVs.

**Figure 5.**The figure shows the distance between the UGV and the obstacle in method (a) and method (b).

**Figure 7.**The picture shows the air-ground system trajectory of method (c) and method (b). In method (c), the UAV first sends out the wrong intervention task (UGV not received), and then sends out effective intervention task (UGV received). The UGV finally reaches the target point. In method (b), the UGV directly receives the wrong trajectory of the intervention task. The UGV encounters obstacles.

**Figure 8.**The figure shows the decision variables of method (c) with the wrong intervention first, then the correct intervention and method (b) with wrong intervention.

**Figure 9.**The figure shows the distance between the UGV and the obstacle in method (c) and method (b).

**Figure 10.**The figure shows the event-triggered performance of the method (c) and method (b), respectively. For method (c), After 18.1s, the UAV continues to send wrong intervention tasks, and the UGV continues to accept the wrong tasks. For method (b), the UAV sends the wrong intervention tasks at 10.6 s, which is refused by the decision maker based on MPC of the UGV. At 13.1 s, the UAV sends a correct intervention task, which is accepted by the UGV.

Parameter | Value | |
---|---|---|

initial position | ${\left[\begin{array}{ccc}1& 0.5& 0\end{array}\right]}^{T}{\left[\begin{array}{ccc}-1& 0.5& 0\end{array}\right]}^{T}{\left[\begin{array}{ccc}0.2& 1& 2\end{array}\right]}^{T}$ | |

obstacles position | $\left[\begin{array}{ccc}1.7& 4& 0\end{array}\right]\left[\begin{array}{ccc}4& 7& 0\end{array}\right]$$\left[\begin{array}{ccc}-0.5& 5& 0\end{array}\right]$ | |

target position | ${\left[\begin{array}{ccc}3.3& 13& 0\end{array}\right]}^{T}$${\left[\begin{array}{ccc}-1& 13& 0\end{array}\right]}^{T}$${\left[\begin{array}{ccc}1.1& 13& 2\end{array}\right]}^{T}$ | |

UGV1 preset trajectories | ${\left[\begin{array}{ccc}3.3& 1t& 0\end{array}\right]}^{T}$ | |

UGV2 preset trajectories | ${\left[\begin{array}{ccc}1.3& 1t& 0\end{array}\right]}^{T}$ | |

safe distance | 2 m | |

task gain A,B,C | 3,2.5,1.5 | |

DDM parameter ${c}_{1}$,${c}_{2}$,${\sigma}_{j}$,${\varsigma}_{j}$ | 0.5,1,10,3 | |

MPC sampling frequence | 20 Hz | |

MPC prediction horizon | 3 s | |

MPC grid point number | 60 |

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**MDPI and ACS Style**

Wang, W.; Guo, J.; Tian, G.; Chen, Y.; Huang, J.
Event-Triggered Intervention Framework for UAV-UGV Coordination Systems. *Machines* **2021**, *9*, 371.
https://doi.org/10.3390/machines9120371

**AMA Style**

Wang W, Guo J, Tian G, Chen Y, Huang J.
Event-Triggered Intervention Framework for UAV-UGV Coordination Systems. *Machines*. 2021; 9(12):371.
https://doi.org/10.3390/machines9120371

**Chicago/Turabian Style**

Wang, Wu, Junyou Guo, Guoqing Tian, Yutao Chen, and Jie Huang.
2021. "Event-Triggered Intervention Framework for UAV-UGV Coordination Systems" *Machines* 9, no. 12: 371.
https://doi.org/10.3390/machines9120371