Research at AVRC
Our research directions connect dynamic systems and networked control with robotic platforms, digital twins, and experimental validation. Publications provide the record of completed scholarly work.
Undergraduate and graduate students participate in faculty-supervised research using the lab’s shared platforms. Students who wish to undertake independent or voluntary research can discuss a suitable question, faculty mentorship, preparation, and lab access with the AVRC team.
These themes describe the lab’s areas of investigation and opportunities for faculty-supervised work.
Investigate learned perception, vision-guided manipulation, and cooperative air–ground robotics using the lab’s shared platforms.
Proposed investigations compare learned methods with conventional baselines and quantify performance and limitations.
Study coordination, consensus, and the effects of network structure and communication on dynamic systems.
A foundation in Dr. Lashhab’s published work on dynamic consensus networks and robust control.
Investigate sensing, localization, path tracking, and navigation using QBot Platform and simulation.
Compare approaches against a stated baseline using repeatable tasks and recorded data.
Explore drone dynamics, estimation, and trajectory tracking through simulation and supervised indoor QDrone 2 experiments.
Define the test envelope, supervision, and stop procedure before physical trials.
Connect kinematics, feedback, sensing, and interaction through QArm and HD2 experiments.
Evaluate tracking, repeatability, or interaction behavior under documented conditions.
Use virtual models to prepare experiments, then examine where measured behavior differs from the model.
Record assumptions, parameter choices, sensor effects, and limits to transfer.
Connect modeling and controller design to physical processes, including the networked and building-system models represented in the publication record.
Relate stability and performance analysis to observable experimental behavior.
Choose a question, learning objective, and measurable outcome.
Explore a model or digital twin and document a baseline.
Test on hardware after orientation and approval.
Compare evidence, explain limitations, and improve the design.
Discuss the literature, available platforms, scope, and the evidence needed to evaluate your idea.