MathWorks Project
MATLAB - Wikipedia

Project Description

Our team will start by gaining proficiency in MATLAB®, Simulink®, UAV Toolbox, and other necessary resources and background material provided to us. Next, we will configure a simulation scenario resembling a cuboid with multiple stationary obstacles, simulating an urban setting, utilizing a UAV Toolbox. We will proceed to create a 3D path-planning algorithm for drone flight that ensures collision-free navigation, taking advantage of the path-planning resources designed for a single drone. To facilitate multiple drones' coordinated movement, we will expand our path-planning algorithm, necessitating centralized tracking of all drones, including their positions and velocities. To initiate this process, we will utilize the ground truth data from simulated drones. We will familiarize ourselves with centralized tracking techniques by referring to the examples in the Background Material Section. Finally, we will evaluate the performance of the algorithm in a cuboid scenario environment involving multiple drone flights.

Motivation

Path planning within the realm of Urban Air Mobility (UAM), encompassing air taxis and drone deliveries, presents a pivotal challenge for the transportation sector. With the exponential growth of UAM, the need for highly efficient and optimized path-planning algorithms is poised to surge. According to the Grand View Research Report, the drone delivery market is projected to reach an estimated $583.51 billion by 2023, while Morgan Stanley predicts the air taxi market will soar to $1.5 trillion by 2040. The development of an efficient path-planning system for UAM has the potential to revolutionize urban transportation, rendering cities more livable and sustainable. Such an algorithm will be instrumental in orchestrating collision-free paths for multiple drones operating within the same environment, all while minimizing time and cost. This project represents a distinctive opportunity to wield cutting-edge technology in addressing the intricate challenges of path planning in UAM, thereby leaving an indelible mark on the future of transportation and logistics.

Scope:

Roles

Faculty Advisor Dr. Manveen Kaur
Project Lead Lara "Jade" de Jesus
Communications Lead Juan Tiguila 
Documentation Lead Abraham Diaz
Landscape Team

Marcos Olvera

Prashant Tewary

Juan Tiguila 

Jade de Jesus

UAV Team

Kevin Velez

Erick Vergara

Abraham Diaz

Algorithm Team

Jason Alvarez

Jonathan Dang

Bryan Segovia

Student Team
  • Jason Alvarez
  • Jonathan Dang
  • Lara De Jesus
  • Abraham Diaz
  • Marcos Olvera
  • Bryan Alfonso Segovia
  • Prashant Tewary
  • Juan Tiguila Sajche
  • Kevin Angel Velez
  • Erick Vergara
Project Sponsor
MathWorks
Project Liaisons
Faculty Advisors
  • Manveen Kaur