# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 6 Conclusions  
In this work, we have proposed a multi-UAV multi-target cooperative reconnaissance scheme. Firstly, based on global information, it adopts a heuristic algorithm for obtaining solutions with greater reconnaissance profits and shorter flight distances than the distributed algorithm. Subsequently, an improved distributed algorithm based on the auction mechanism is employed to dynamically adjust the allocation scheme based on real-time environmental changes, compensating for the defect of centralized algorithms in processing dynamic scenarios. Simulation results demonstrate that the proposed multi-UAV multi-target collaborative reconnaissance scheme effectively solves the task allocation problem for multiple UAVs and targets, and effectively handles unexpected situations such as the emergence of new tasks and UAV damage. Furthermore, the improved distributed algorithm significantly reduces the flight distance cost of the UAV compared to the traditional distributed algorithm, resulting in greater reconnaissance gains.  
The primary contribution of this paper is to introduce and investigate a dynamic scenario model that considers UAV damage and the emergence of new reconnaissance areas. Additionally, an innovative collaborative multi-target and multi-UAV reconnaissance scheme is proposed to address the problem, which effectively solves the task reassignment problem when the UAV is damaged or a new mission area appears. Finally, we conduct simulations to validate the effectiveness of the algorithm.  
Our work addresses the allocation of UAV cooperative reconnaissance missions in dynamic situations. However, it does not take into account certain communication challenges that may arise in practical applications, nor does it consider the requirements of heterogeneous target reconnaissance in complex scenarios. Specifically, the reconnaissance of heterogeneous targets necessitates the use of various types of UAVs for target reconnaissance. The inclusion of multiple UAV types and target categories introduces a more complex scheduling optimization problem, producing a challenge to scheduling algorithms. In future work, we plan to incorporate the communication status among UAVs into our planning process and explore cooperative strategies for both heterogeneous targets and UAVs to meet the demands of even more complex reconnaissance scenarios.  
Conceptualization, Q.L. and M.Z.; methodology, Q.L. and M.Z.; software, Y.Q.; validation, Q.L. and Y.Q.; formal analysis, C.G.; investigation, H.W.; data curation, H.W., Q.L. and Y.Q.; writing--original draft preparation, Q.L.; visualization, H.W. and Y.Q.; project administration, B.X., W.L. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by National Key R&D Program of China (2021 ZD011502), National Natural Science Foundation of China (51907112, U2066212, 61961036, 62162054), Jiangxi \"Double Thousand Plan\" Project (JXSQ20210019), Natural Science Foundation of Guangdong Province of China (2021A1515011709), Scientific Research Starting Foundation of Shantou University (NTF20009), Open Project for Guangxi Key Laboratory of Machine Vision and Intelligent Control (2022B04), Natural Science Foundation of Guangxi (2020J)(A170007), Special Project of Guangxi Science and Technology Base and Talent (Guike AD20297148), Guangxi Innovation Driven Development Project (Guike AA18118036). The funding bodies have played a role in the purchase of experimental equipment and expert consultation.  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflict of interest.