ECE Robotics Lab
Room 364, Engineering I Building
Electrical and Computer Engineering
University of Central Florida
4000 Central Florida Blvd.
Orlando, Florida 32816
Faculty MembersAman Behal (ECE), Michael Haralambous (ECE), Kurt Lin (MAE), Zhihua Qu (ECE), and Yunjun Xu (MAE)
Lab DirectorZhihua Qu
The Robotics Lab aims at developing optimization and control algorithms, software and hardware that enables autonomy of unmanned (aerial, ground, surface and underwater) vehicles, energy harvesting from ocean waves, manufacturing automation, space robotics, and other robotic applications.
The research activities at the Lab have been supported by National Science Foundation (NSF), Department of Defense (DoD), Air Force Office of Scientic Research (AFOSR), Air Force Research Laboratory (AFRL), Army Research Development and Engineering Command (RDECOM), Army Research Laboratory, NASA Kennedy Space Center, Harris Corporation, Lockheed Martin Corporation, L-3 Communications Link Simulation & Training, Oak Ridge National Laboratory, Science Applications International Corporation (SAIC), Florida High Tech Council (FHTC), and Florida Space Grant Consortium (FSGC).
(supervised by Prof. Qu)
M.Sc. Electrical Engineering -
University of Central Florida
B.Sc. Computer Engineering -
University of Florida
Laboratory Facility & Equipment
B.Sc. Mechanical Engineering-
University of Central Florida
The available equipment in the laboratory are:
• An ATRV-Jr mobile robot platform from iRobot
• Robotic manipulators (SCARA direct-drive arm, Puma 560 manipulator, Adept arm, et al.)
• A 2000kg 6-DOF electric motion platform
• Design and analysis software (Matlab/Simulink, Labview and Labwindows, AutoCAD, etc.)
• An autonomous optical character recognition (OCR) system for manufacturing
Figure 1: ATRV-Jr All-Terrain Mobile Robot
Among the three platforms, the ATRV-Jr from iRobotTM is an all-terrain mobile robot, the only system purchased as a whole because it is commercially designed for the purpose of research and development. Shown in figure 1,the robot has an on-board computer, a suite of sensors (including a compass, a sonar array, a high-performance vision system with pantilt-zoom control, an inertial navigation system, a differential GPS system), a wireless/radio system (including a base station, a mobile station, and antennas), and safety devices (tactile bumpers, an emergency system, and backup units).
Figure 2: A Team of Mini Rovers
Figure 3: A wireless controlled Mini Rover
Figure 4: Robotic Manipulator
Figure 5: Robotic Wafer Handling System
The second robotic platform consists of 6 all-wheel-drive rovers shown in figures 2 and 3, a group shot and a close-up image. These mini rovers are equipped with a 4-axis microcontroller, a fast wireless communication module unit, an optical encoder, a digital compass, and a micro inertia measurement unit (for 3 out of 6 rovers). A mini gripper can also be installed. To reduce size, weight and development cost and to increase flexibility, the rovers are not made to be truly autonomous themselves, instead they are wirelessly controlled by a host computer. In the host computer, information of individual rovers are shared according to their appropriate trajectory planning and control algorithms. For example, in simulating a dynamic and uncertain environment, a few of the rovers are designated to play the role of "moving obstacles," these "obstacles" are controlled open-loop according to any prescribed trajectories. The trajectories of the "obstacles" are not available to any robotic vehicles, the current position and velocity of an "obstacle" (or one rover) are passed to a specific rover only if it enters into the "sensing" range of the rover, and the rover replants its trajectory (and/or changes its control) to account for the "uncertainty." By enabling the control of feedback information, the host computer creates whatever environment is designed, simultaneously and independently undertakes the planning and control functions of each rover, and becomes the arbiter in quantifying the overall system performance.
Figure 6: Cylindrical Manipulator
To simulate many applications in manufacturing automation, a cylindrical robotic manipulator was designed and assembled. As shown in this figure 6, cylindrical robotic manipulator has 3 degrees of freedom (two translational and one rotational) and a gripper as the end effector. It is controlled by a host computer with a PCI-7344 motion controller card from National Instrument. It is built using off-the-shelf components (Galil servo motors, PWM power amplifiers with torque-control mode, Lintech manipulator, standard camera).
Figure 6: Uni-directional Impulse Turbine
A low speed bi-directional wind tunnel is used to experiment transient air loads experience in an oscillating water column. Uni-directional turbine built to harness bi-directional airflow created by ocean waves. Online sensing and monitoring allow for smart generator load control in transient and fluctuating input loads. It is controlled by an RPM sensor, torque transducer and pressure probes upstream and downstream of the turbine section.
The ATRV-Jr mobile robot, the team of mini rovers, and their combinations provide the ideal platforms to conduct experiments in the following topics:
• Real-time trajectory planning for collision/obstacle avoidance.
• Advanced control strategies for robotic vehicles (e.g., formation control, near-optimal control of nonholonomic systems).
• Cooperative control and behaviors.
• Patrolling and coverage control.
• Swarming, machine intelligence, evolutionary computing, and rule-based controls.
The cylindrical manipulator is used to facilitate coverage of the following subjects:
• Derivation and simulation of kinematic and dynamic equations.
• Design, simulation, and implementation of rigid-body robot control methods (classical controls, computed torque control, adaptive control, robust control, and force control).
• Visual servoing and vision-feedback control systems.
Ocean energy harvesting:
• Experimental oscillating buoy for wave energy conversion.
• Load control optimization in transient turbine generator loads.
• Computational fluid dynamics for turbine control mode.