Author(s)

Fengbin Chen

Corresponding Author

ABSTRACT

After the natural disaster, in addition to the disaster itself, the affected area also faces huge challenges, that is, the collapse of cellular communications, power and transportation networks has brought a lot of inconvenience to the disaster relief work. The surge in demand for medical supplies and the restoration of roads are two aspects that cannot be ignored. The reasonable solution of the above two problems will not only help improve the efficiency of rescue, but also promote the post-disaster reconstruction process. The survey of the main roads in the affected areas is the first step in solving these two problems. In this article, a series of methods have been developed to help plan the route of the survey task. To find the best reconnaissance lines, we learn from the Traveling Salesman Problem (TSP), and then roughly get the shortest flight routes of the drones carrying the aerial photography device based on the graph and network model. Due to the inevitable errors, we have improved them through the Simulated Annealing (SA) algorithm to achieve the purpose of optimization. We selected Puerto Rico as the research object. By collecting information about Puerto Rico's geography, climate, major cities and roads, and using the methods described above, the best route for the survey mission was obtained. Through verification, we conclude that this model can solve relatively optimal route planning.

KEYWORDS

Route planning; TSP; SA