Home
Scholarly Works
Drone's trajectory optimization for searching a...
Conference

Drone's trajectory optimization for searching a disaster collapsed area using data mining techniques

Abstract

Drones have unique characteristics such as mobility, flexibility, and role adaptability. Hence, they are useful tools during urban search and rescue (USAR) operations in areas where many buildings have suffered structural collapse leading to difficulties in reaching locations where people may be trapped. However, drones are limited by the life of the batteries allowing their flight. Clearly, reducing flight time to useful destinations can help to optimize the use of drones with limited battery life. Therefore, this study considers trajectory optimization as a critical research problem for USAR applications. This paper explores optimal trajectory paths for USAR operations for a single drone by selecting nodes within a disaster area from the start point to the destination point for the drone's flight. The following analysis applies density-based spatial clustering of applications with noise (DBSCAN) data clustering algorithm to the dataset obtained from the scanning from the top of the disaster collapse area. The goal is to find the optimized shortest paths for USAR drone operations in order to conserve drone flight time. The computational results are compared to physical experimental results.

Authors

Islam AZ; Hanna D; Ferworn A

Volume

00

Pagination

pp. 32-38

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication Date

December 10, 2022

DOI

10.1109/iccicc57084.2022.10101671

Name of conference

2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
View published work (Non-McMaster Users)

Contact the Experts team