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1、研究生英语学术写作研究生英语学术写作期中作业期中作业学号： 姓名：1. Baic InformationTitle: Multi-UAV Routing for Persistent Intelligence Surveillance these models are akin to the models in 14 and 12 used to solve the traveling salesman problem with time windows and the distance constrained vehicle routing problem. Similar formulat
2、ions were also used to solve fuel constrained multiple vehicle routing problem in 13. In these articles, the constraints on the length of a tour starting from a depot are constrained. In the formulation presented here, the length of a tour starting from a depot to each task and the length starting f
3、rom the task returning to the depot together are constrained.We model the using Similar formulations were also used to solve problem in In these articles, are constrained.The problem can be stated as the following: find at most cycles that minimizes the maximum vndelivery time such that, each task T
4、 is covered by one cycle, and if a task assigned to ( ) i( )iiitone of the UAVs, with cycle length , then .vvL,viLRiT The problem can be stated as the following: ( ) i( )iiIn the above formulation, the in the constraints (4), (6) is known to cause computational bigM problems 14, 17, and hence make t
5、he MILP model computationally less efficient. We propose a second formulation without constraints and compare the computational performance of bigM these two formulations. In the above formulation, is known to cause computational problems We propose a second formulation and compare the computational
6、 performance of these two formulations.IV. ASSIGNMENT TREE SEARCH HEURISTICIn this section, we present a heuristic to solve the PISR routing problem. The heuristic is a greedy assignment tree search, based on the prior work in 18, 19, for planning missions involving multiple UAVs. Here, we present a
7、 synopsis of the tree search algorithm, however one can refer to18, 19 for further details. This tree search follows a best first search pattern until it finds a feasible assignment. In this section, we present a . to solve . problem Here, we present a synopsis of the tree search algorithm, however
8、one can refer to 18, 19 for further details. This tree search follows a best first search pattern until it finds a feasible assignment.We use this tree search heuristic to find feasible paths for the PISR routing problem. The algorithm is adapted to find feasible paths, such that the cycle lengths o
9、f each UAV adheres to the revisit period constraints of the tasks the UAV is assigned, and minimizes the maximum delivery time of all the tasks. We use this tree search heuristic to find feasible paths for the PISR routing problem. The algorithm is adapted to find feasible paths, such that the cycle
10、 lengths of each UAV adheres to the revisit period constraints of the tasks the UAV is assigned, and minimizes the maximum delivery time of all the tasks.Next, we discuss how the analysis helps identify high-impact sites that are candidates for additional hardening or redundancy, and how it also ide
11、ntifies low-impact sites whose loss does not dramatically reduce performance. The results in Fig. 5 show that we have a single site whose loss reduces the user coverage from 95.7 % to 92.7 %. In Fig. 6, we show the location of this site. We also show, in green and red respectively, the regions of ab
12、ove- and below-threshold coverage with the site lost. Comparing this figure to Fig. 4b, we observe that the negative impact is confined to the area around Congress Heights and Joint Base Anacostia-Bolling in the southeastern portion of the city. Also, the decrease in coverage is due to the relative
13、isolation of this site; the presence of an additional site would be likely to reduce the impact of the sites loss. Next, we discuss how helps identify The results in Fig. 5 show that In Fig. 6, we show the Culling low-impact sites also reduces the ability of the network to handle large-scale inciden
14、ts that require the deployment of large numbers of public safety resources. To demonstrate this, we simulated the gas leak incident use case defined by NPSTC in 7, which models the “report of a toxic gas leak in a large public assembly building near the National Mall in Washington, DC.” We considere
15、d 118 possible locations for the incident, which we show in Fig. 9. At each location, the incident occupies a 1.6 km 1.6 km square. The incident command personnel are concentrated in a small area; the remainder of the 327 responders and 127 vehicles that compose the response force are deployed unifo
16、rmly in the incident area. We simulated background traffic associated with routine public safety operations, and superimposed it on the incident traffic. We used the mix of applications and associated offered loads in Exhibit 9 on p. 26 of 8, but reduced by half, to generate the background traffic. To demonstrate this, we simulated the We simulated We used the mix of
