Date of Publication

2021

Document Type

Master's Thesis

Degree Name

Master of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Electronics

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Honor/Award

Outstanding Thesis Award

Thesis Advisor

Edwin Sybingco

Defense Panel Chair

Maria Antonette C. Roque

Defense Panel Member

Leonard U. Ambata
Alvin Y. Chua

Abstract/Summary

The research develops a path planning trajectory using the particle swarm optimization (PSO) for unmanned aerial vehicle (UAV) application. In order to create a practical trajectory, a cost function containing the environmental constraints and trajectory characteristics are used. The main characteristics being studied are the surveillance area importance (SAI), energy consumption (EC), and flight risk (FR). A trajectory having a high SAI value, low EC and FR are desirable for an autonomous UAV to use. Using PSO, trajectories for three UAVs are being generated to be used to reach a target location. For post disaster applications, it can be useful to generate a path planning trajectory for a drone pilot to use instead of manual flight. In this study, assuming a mountain environment with a landslide scenario, the PSO algorithm computes for the best path the UAVs can take to maximize the area of interest (SAI), minimize the battery consumption (EC) and the risk of flight (FR). In order to compare the performance of the PSO generated trajectories, a genetic algorithm (GA) based trajectory was also created. The results presented that the PSO generated paths has the better trajectory characteristics as compared to the GA.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

136 pages, illustrations (some color)

Keywords

Trajectories (Mechanics); Drone aircraft

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Embargo Period

5-31-2021

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