A research project and student team building a world-class testbed for swarm robotics and intelligence — where collective behavior emerges from local interactions, and where engineering and mathematics are equally at home.
How does useful collective behavior emerge from simple local interactions among autonomous agents? And how do we deliberately design communication architectures and control structures that make reliable, scalable coordination possible?
These bottom-up and top-down perspectives are mirrors of each other. The most interesting science lives at their intersection — and this lab is built to probe it experimentally, with over 100 real robots.
Bottom-up: Local rules, emergent global behavior, distributed intelligence.
Top-down: Designed architectures, formal guarantees, principled coordination.
Designing controllers with formal guarantees — stability, robustness, and performance — under real-world disturbances and model uncertainty.
From graph search to trajectory optimization: finding paths that are safe, dynamically feasible, and optimal with respect to a task objective.
Fusing noisy sensor measurements into accurate, uncertainty-aware state estimates — the foundation every other stack layer depends on.
Graph-theoretic and distributed optimization tools for designing protocols that produce coherent collective behavior at fleet scale.
Integrating ML and classical engineering with honest attention to interpretability, safety guarantees, and when learned components help or hurt.
Biology, music, choreography, and social science each offer insight into collective behavior that pure engineering cannot generate on its own.
Coordinated multi-robot systems are moving from research labs into the world at speed. The theory and infrastructure built here connects directly to consequential applications.
Autonomous inspection of turbines and transmission lines — including the large-scale wind infrastructure right here in Texas.
Coordinated multi-vehicle navigation without central controllers — intersection negotiation, lane merging, shared road space.
Distributed sensor networks for ocean chemistry, atmospheric composition, wildfire behavior, and climate variables at scale.
Coordinated robot fleets in hospitals, warehouses, and buildings — delivery, navigation, and human-safe autonomous operation.
Coordinated UAV teams for search, mapping, and logistics in contested environments where decisions must be made at machine speed.
Ground and aerial robot coordination for crop monitoring, irrigation management, and disease response at field scale.
Alongside the research infrastructure work, the team pursues a motivating long-horizon goal: a lab that never sleeps. Robots that perform, recharge, and perform again — autonomously, indefinitely.
Three or more Crazyflies in the air simultaneously with light decks active. The moment the lab becomes real for the first cohort.
8–12 Crazyflies holding a geometric formation under OptiTrack feedback with synchronized light sequences.
15+ robots in a 2–3 minute choreographed show: formation transitions, spelling in mid-air, synchronized light cues.
Crazyflies and Turtlebots performing together — aerial and ground robots coordinated in a single unified show.
Generative, continuously varying performance running autonomously whenever the lab is idle. Robots cycle through charging and flight indefinitely.
The project is led by three faculty members from the Erik Jonsson School of Engineering and Computer Science at UT Dallas.
Associate Professor, Mechanical Engineering. Research in control, optimization, and learning in complex dynamical systems.
Assistant Professor, Systems Engineering, Research in network controls, graph machine learning, and multi-agent systems
Associate Professor, Mechanical Engineering. Research in controls, model predictive control, and energy systems.
Graduate student coaches and project members will be listed here as the first cohort is formed.
Imagine a fleet of drones coordinating a disaster response — each one making local decisions that add up to something intelligent and purposeful. In Autonomous Fleet Systems, you will tackle these problems as part of a close-knit, intergenerational team where freshmen, seniors, and PhD students work side by side.
No prior robotics experience is required — just curiosity and a willingness to contribute. Each semester builds on the last, and the relationships you build along the way are as much a part of the experience as the engineering itself.
Math-first. For students who want to understand the proofs.
Hardware, sensors, OptiTrack pipeline.
Single-agent autonomy stack, trajectory generation.
Coordination protocols, distributed algorithms.
Learned components, system identification, safety.
Unity integration, experiment design, analysis.
Biology, music, choreography, social science — all disciplines welcome.
Students enroll for course credit through the ECS RIDE program. Effort scales with credit hours — about 3 hours of work per credit hour per week.
Start at 1–2 credits and increase as you become more deeply involved. Multi-semester participation is strongly encouraged.
Students at any level — freshman through senior — from any major. Engineering, CS, math, physics, biology, music, art, and social science students have all found meaningful roles in projects like this one.
The project is designed to be accessible at multiple levels of preparation. You do not need to know ROS or control theory before you start.
Interested? Fill out the interest form or reach out directly.
Express Interest