Drone Swarm Project – CSE Senior Design

Team Name

Drone Swarm Team!

Timeline

Spring 2024 – Summer 2024

Students

Abhisekh Bajracharya – CS
Ayman Abdalla – SE
Gavin Meyer – CS
Richard Olu Jordan – CS
Rumaysa Jafer – CS

Sponsor

Professor McMurrough/Professor Conly

Abstract

The Drone Swarm Project aims to enable dynamic flight formations and real-time control adjustments through advanced technologies like motion capture systems and GPS-independent navigation. By integrating hardware and software components, the project achieves precise drone tracking, allowing for GPS-independent experiments and complex formations. The Crazyflie 2.1 was selected for its flexibility and strong community support. Software development utilizes Python and tools like CrazySwarm to manage flight patterns, incorporating algorithms for real-time control and autonomous adjustments. The project employs the Robotic Operating System (ROS) on Ubuntu to facilitate inter-process communication.

Background

Drone technology has seen dramatic advancements in the last few decades. The evolution of drone technology has significantly affected various sectors, including surveillance, delivery services, entertainment, and research. Drones have become more accessible and useful, demonstrating their potential in a wide range of applications. This Drone Swarm Project aims to explore the frontiers of drone technology by focusing on innovative solutions to current technological limitations. Current drone technologies are heavily reliant on GPS for navigation, which limits their functionality in environments where GPS signals are weak or unavailable, such as indoor spaces or densely populated urban areas. Additionally, the ability to perform complex flight formations is constrained by these navigational challenges. These limitations restrict the potential applications of drones, particularly in areas that could benefit from their use. The project proposes the use of motion capture systems as an alternative to GPS for navigation, enabling drones to operate in GPS-denied environments. Additionally, the project will develop algorithms for dynamic flight formations, allowing drones to perform more complex tasks. This approach not only addresses the limitations of current drone technologies but also opens new opportunities for drone applications in areas previously considered inaccessible.

Project Requirements

Flight Area
Scripted Flight Path
Vicon Motion Tracking System
Communication Reliability
Trajectory Accuracy
Documentation
Startup and Shutdown Times
Operation Speed
Dynamic Flight Maneuvers
Fault Tolerance

Design Constraints

Timing: In the instance the drones arrive later than anticipated, we need to plan extra time for unexpected delays and communicate effectively to manage expectations.
Location: Given that our team members all reside away from campus, face-to-face meetings will be infrequent. This distance could potentially delay our meeting times and collaboration. To overcome this obstacle, we are committed to using online platforms such as Discord, Messenger, and Microsoft Teams to ensure collaboration, enabling us to progress effectively.
Lack of Experience: None of us have drone knowledge, which could lead to challenges in understanding and implementing the project requirements. We must dedicate time to learning about drones through online resources, tutorials, and workshops, and be proactive in seeking guidance from tutorial videos on platforms such as YouTube or Coursera.
Costs: The price per drone is around a third of our budget. The allocation of funds is crucial, so that we can stay close to the budget amount.
Environmental: we have to adapt the drones to different environments and conditions. Some of which could include obstacles within the flight area, while avoiding interference from other electronic devices or signals.

Engineering Standards

NFPA 101: The flight area must be cleared of any obstacles other than the obstacles placed for the second flight phase.

Occupational Safety and Health Standards 1910.147 (The control of hazardous energy) (lockout/tagout): Equipment usage, due to lock removal policies, will be limited to availability of the course instructor and designed teaching assistants.

Federal Aviation Administration (FAA) (general flying guidelines during outdoor use): During the event of a crash, extra caution is to be used when recovering equipment and the Lab Manager
should be notified and reported. Safety goggles, gloves, and proper attire are required to be worn when entering the drone flight cage.

FCC Standard Frequency: The flight path must be designed to avoid collisions between drones and maintain a minimum safe distance from obstacles and the surrounding structure.

NFPA 70: High voltage power sources, as defined in NFPA 70, will be avoided as much as possible to minimize potential hazards.

System Overview

The Drone Swarm Project aims to develop a system that enables multiple drones to fly in dynamic formations, utilizing advanced technologies such as the motion capture system Vicon, and GPS-independent navigation algorithms. The primary components of the system include:

Crazyflie 2.1 Drones: Open-source drones capable of GPS-independent flight.
Motion Capture System: Vicon system installed within a dedicated enclosure, providing precise tracking of drones in flight.

Software Integration: Utilizes Python code interfacing with the motion capture system and implementing real-time control algorithms.
Real-time Control: Algorithms enabling drones to autonomously adjust flight paths and formations.

Results

Future Work

Going forward, we will expand our project to include a more robust implementation of motion capture technology, specifically utilizing the Vicon tracker with reflective markers. Additionally, we will enhance the functionality of the drone swarm project by incorporating dynamic flight capabilities, such as obstacle avoidance algorithms.