Student project pathways
The lab supports faculty-supervised research, Vertically Integrated Projects (VIP), senior design, and capstone work across Howard’s engineering and computing departments. Project scope and platform access are coordinated with the faculty mentor and lab team.
Computer science senior design teams have begun work on multi-agent aerial and ground robotics, including leader–follower coordination. The projects address collaboration between vehicles within the lab’s shared experimental environment.
This work is in progress. Experimental results will be reported as the teams complete and document their investigations.
Fadel Lashhab’s conference demonstration connects a QLabs digital twin with physical QArm operation through a model-based pick-and-place sequence. The implementation uses waypoint sequencing, rest-to-rest cubic task-space trajectories, inverse kinematics, and forward-kinematic checks, with hardware deployment through QUARC.
Related student exercises, platform examples, and technical documentation are available in the public academic resource repository.
Develop a sustained team effort with documented onboarding, shared code, and clear handoffs between student cohorts.
Teams across engineering and computing departments can translate requirements into a feasible prototype, a test plan, and evidence that supports their conclusions. Activities are arranged with the course instructor and faculty mentor.
Reproduce a baseline from the literature, identify a focused question, and evaluate a method under stated conditions.
Illustrative project ideas; these are not announcements of completed projects or guaranteed openings.
Use QBot or a virtual environment to compare path tracking, localization, or obstacle-response strategies.
Investigate QArm kinematics, repeatability, or a supervised pick-and-place task.
Compare controller designs on QUBE-Servo 3 or Aero 2 using consistent performance measures.
Develop simulation and supervised indoor validation around tracking or estimation with QDrone 2.
Study how communication assumptions affect a small multi-agent control problem.
Develop an accessible demonstration with a guide, learning objectives, and a reproducible setup.
Explore six proposed investigations in robot perception, air–ground leader–follower coordination, cooperative inspection, multi-agent systems, QArm visual manipulation, and learning from simulation. Each includes a research question, a practical starting point, and evaluation measures.
Question, scope, requirements, team roles, and faculty mentor.
Software versions, wiring or configuration, and baseline code.
Experiment conditions, data, plots, failures, and limitations.
A concise report, demonstration, and next-step recommendations.
Discuss the learning goals, platform, team size, and supervision needed for a practical lab experience.