Excellence in Research

Project Overview

This project develops a novel framework for swarm robotics by integrating Reinforcement Learning (RL) with Linear Temporal Logic (LTL) to synthesize control policies for unmanned aerial vehicles (UAVs). The main objective is to enable swarms of UAVs to safely and efficiently navigate unknown environments through robust coordination, collision avoidance, and real-time adaptability.

Intellectual Merit

The research introduces a unique method of translating temporal logic specifications into RL reward functions, advancing the precision and reliability of swarm control. By combining distributed coordination strategies with dynamic policy optimization, the project addresses fundamental challenges in multi-agent systems—such as uncertainty, complex dynamics, and real-time responsiveness. The outcomes will contribute significantly to the specification, verification, and control of autonomous swarms, with broader applicability to other multi-agent domains.

Broader Impacts

Anchored at Alabama A&M University (an HBCU), the project empowers underrepresented students by engaging them in advanced AI, robotics, and machine learning research, inspiring future graduate studies and careers. Educational integration into computer science and engineering curricula provides hands-on learning opportunities, while the technology itself supports safer, more efficient drone operations in real-world applications. By broadening participation and fostering diversity in STEM, the project contributes to building a more inclusive innovation ecosystem.

Team & Publications