Webpage: http://s2.smu.edu/~camp/courses/ece5393_taos/index.html
Instructor: Professor Joseph Camp
Email: "camp" AT "smu.edu"
TAs: Farhad Nouri (farhadn@) and Fred Solis (fsolis@)
Office hours: See schedule or otherwise arranged.

Time/Place:
The course will meet each weekday for the following schedule:

• Morning Session: 9:00am - 12:00pm
• Afternoon Session: 1:00pm - 2:00pm

Course Description: In this immersive course, students will explore autonomous drone technologies, focusing on multi-agent systems and AI-driven drone coordination. Emphasis will be placed on autonomous mission planning, drone communication, and the integration of AI techniques such as federated learning, explainable AI, and homomorphic encryption. Students will have the opportunity to work with real drone hardware and cloud-based simulations, learning how to process LiDAR data, train machine learning models, and develop drone coordination algorithms.

The course is accessable at two levels:

• Undergraduate Level: Basic principles of autonomous drone technology, including machine learning, multi-agent coordination, and geospatial data processing using Python libraries.
• Graduate Level: Students will work on real-world challenges involving federated learning, collaborative drone teams, and AI-powered mission planning. Students in the advanced track will contribute to ongoing research and publication efforts in top-tier AI and robotics conferences.

Background: Students must be proficient in computer programming.

5393 Grading:

70% Activities
5% Project Proposal
10% Project Final Code and Data
15% Project Final Presentation

25% Project Proposal
25% Project Final Code and Data
50% Project Final Presentation

You may discuss Activities with classmates but all solutions must be original and individually prepared. Late lab work will be penalized at 15% of its full credit per day up to a maximum of 4 days, after which no late work will be accepted.

The SMU Honor Code will be strictly enforced.

Required Textbooks: None.

Other Resources:

Topics:

• Introduction to Autonomous Drones and AI Integration
• Wireless Communication for Drones and Emerging Technologies
• AI for Wildfire/Weed Anomaly Detection with Real-world Datasets
• Research and Innovation in Autonomous Systems
• Geospatial Mapping and Machine Learning with QGIS and DeepForest
• Object Detection for Aerial Platforms (YOLO, VisDrone)
• Multi-Agent Systems and Reinforcement Learning
• Digital Twins and Virtual Environments for Drones
• Homomorphic Encryption for Secure Drone Communications
• Project Design and Research Implementation

Religious Observance:
Religiously observant students wishing to be absent on holidays that require missing class should notify their professors in writing at the beginning of the semester, and should discuss with them, in advance, acceptable ways of making up any work missed because of the absence. (See University Policy No. 1.9.)

Excused Absences for University Extracurricular Activities:
Students participating in an officially sanctioned, scheduled University extracurricular activity should be given the opportunity to make up class assignments or other graded assignments missed as a result of their participation. It is the responsibility of the student to make arrangements with the instructor prior to any missed scheduled examination or other missed assignment for making up the work. (University Undergraduate Catalog)

Student Learning Outcomes (ABET/SACS):
(I): Ability to apply knowledge of mathematics, science, and engineering
(II): Ability to identify, formulate, and solve engineering problems
(III): Ability to communicate effectively
(IV): An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice