# Multi-UAV Mission Allocation under Constraint
## 6 Conclusions and Future Works  
An improved model of the multi-UAV mission allocation problem is presented, which considers time windows, reconnaissance reward, target heterogeneity, and reconnaissance resource allocation. The novelty of the model is mainly reflected in the following three aspects:  
1. We apply the concept of scouting income to the model and use it as a decision-making indicator to measure the pros and cons of the final decision;  
2. The emergence of reconnaissance income makes the importance of each target area fully reflected and more in line with actual needs;  
3. Based on considering the above two points, we add time constraints, making the whole model closer to actual operational needs.  
The optimization objective is that the flight path should be as short as possible, and the reconnaissance reward should be maximized. The applications in Lingo software can quickly and efficiently solve the improved model. The proposed model is applicable to other software, such as MATLAB (The MathWorks, Natick, MA, USA) and Python ([PERSON], Netherlands). But what we need to emphasize is that we have implemented some processing for the model in the simulation process, which has made the simulation faster and more efficient. The models and methods we propose are common to task assignment-related issues. The proposed model can be more in line with actual needs. Based on guaranteeing time constraints and benefit constraints, it can make the time of each task-executing task more balanced, which makes the collective resource allocation and task completion efficiency greatly improved.  
For future work, it will be widely followed on the further heterogeneity of the combat mission area and the online redistribution of tasks. Different reconnaissance strategies will be adopted for different combat mission areas, and further consideration will be given to coordinating reconnaissance missions by multiple UAVs.  
It should be noted that the Lingo software can perform simulation operations separately. However, with the help of MATLAB software, MATLAB can directly call the Lingo API, and the two interact through the txt file. It can make a simulation process, subsequent drawing, tabulation, and result analysis be more convenient. In the follow-up study, we will consider the decision-making optimization under different intents, once the strategic intent is determined, different Lingo modules can be directly called to obtain the optimization result quickly.  
Conceptualization, S.X., A.Z. and Y.T.; Data curation, W.B.; Formal analysis, S.X. and Y.T.; Funding acquisition, W.B.; Investigation, S.X.; Methodology, S.X.; Project administration, A.Z.; Resources, A.Z.; Software, S.X.; Supervision, A.Z. and W.B.; Validation, S.X., A.Z. and W.B.; Visualization, S.X., A.Z. and Y.T.; Writing--original draft, S.X.; Writing--review and editing, S.X. and Y.T.  
This research was funded by the Key Laboratory Open Foundation of Data Link Technology of CETC, grant number CLDL-20182113.  
The authors would like to thank the editor and the three anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.