Robotics, Intelligent Sensing and Control Lab (RISC)

1. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Robotics, Intelligent Sensing and Control Lab (RISC)

2. Faculty, Staff and Students Faculty: Prof. Tarek Sobh Staff: • Lab Manager: Abdelshakour Abuzneid • Tech. Assistant: Matanya Elchanani Students: • Raul Mihali, Gerald Lim, Ossama Abdelfattah, Wei Zhang, Radesh Kanniganti, Hai-Poh Teoh, Petar Gacesa.

3. Objectives and Ongoing ProjectsRobotics and Prototyping • Prototyping and synthesis of controllers, simulators, and monitors, calibration of manipulators and singularity determination for generic robots. • Real time controlling/simulating/monitoring of manipulators. • Kinematics and Dynamics hardware for multi-degree of freedom manipulators.

4. Objectives and Ongoing ProjectsRobotics and Prototyping • Concurrent optimal engineering design of manipulator prototypes. • Component-Based Dynamics simulation for robotics manipulators. • Active kinematic (and Dynamic) calibration of generic manipulators • Manipulator design based on task specification • Kinematic Optimization of manipulators. • Singularity Determination for manipulators.

5. Objectives and Ongoing Projects Robotics and Prototyping (cont.) • Service robotics (tire-changing robots) • Web tele-operated control of robotic manipulators (for Distance Learning too). • Algorithms for manipulator workspace generation and visualization in the presence of obstacles.

6. Objectives and Ongoing ProjectsSensing • Precise Reverse Engineering and inspection • Feature-based reverse engineering and inspection of machine parts. • Computation of manufacturing tolerances from sense data • Algorithms for uncertainty computation from sense data • Unifying tolerances across sensing, design and manufacturing • Tolerance representation and determination for inspection and manufacturing. • Parallel architectures for the realization of uncertainty from sensed data • Reverse engineering applications in dentistry. • Parallel architectures for robust motion and structure recovery from uncertainty in sensed data. • Active sensing under uncertainty.

7. Objectives and Ongoing ProjectsHybrid and Autonomous systems • Uncertainty modeling, representing, controlling, and observing interactive robotic agents in unstructured environments. • Modeling and verification of distributed control schemes for mobile robots. • Sensor-based distributed control schemes (for mobile robots). • Discrete event modeling and control of autonomous agents under uncertainty. • Discrete event and hybrid systems in robotics and automation • Framework for timed hybrid systems representation, synthesis, and analysis

8. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Prototyping Environment for Robot Manipulators Prof. Tarek Sobh

9. To design a robot manipulator, the following tasks are required: • Specify the tasks and the performance requirements. • Determine the robot configuration and parameters. • Select the necessary hardware components. • Order the parts. • Develop the required software systems (controller, simulator, etc...). • Assemble and test.

10. The required sub-systems for robot manipulator prototyping: • Design • Simulation • Control • Monitoring • Hardware selection • CAD/CAM modeling • Part Ordering • Physical assembly and testing

11. Robot Prototyping Environment

12. Closed Loop Control

13. PID Controller Simulator

14. Interfacing the Robot

15. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Manipulator Workspace Generation and Visualization in the Presence of Obstacles Prof. Tarek Sobh

20. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Industrial Inspection and Reverse Engineering Prof. Tarek Sobh

21. What is reverse engineering? Reconstruction of an object from sensed information.

22. Why reverse engineering? • Applications: • Legal technicalities. • Unfriendly competition. • Shapes designed off-line. • Post-design changes. • Pre-CAD designs. • Lost or corrupted information. • Isolated working environment. • Medical. • Interesting problem • Findings useful.

23. Closed Loop Reverse Engineering

24. A Framework for Intelligent Inspection and Reverse Engineering

25. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Recovering 3-D Uncertainties from Sensory Measurements for Robotics Applications Prof. Tarek Sobh

26. Propagation of Uncertainty

27. Refining Image Motion • Mechanical limitations • Geometrical imitations

28. Fitting Parabolic Curves

29. 2-D Motion Envelopes

30. Flow Envelopes

31. 3-D Event Uncertainty

32. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Tolerancing and Other Projects Prof. Tarek Sobh

33. Problem A unifying framework for tolerance specification, synthesis, and analysis across the domains of industrial inspection using sensed data, CAD design, and manufacturing.

34. Solution We guide our sensing strategies based on the manufacturing process plans for the parts that are to be inspected and define, compute and analyze the tolerances of the parts based on the uncertainty in the sensed data along the different toolpaths of the sensed part.

35. Contribution We believe that our new approach is the best way to unify tolerances across sensing, CAD, and CAM, as it captures the manufacturing knowledge of the parts to be inspected, as opposed to just CAD geometric representations.

39. University of Bridgeport • Department of Computer Science and Engineering • Robotics, Intelligent Sensing and Control • RISC Laboratory Sensing Under Uncertainty for Mobile Robots Prof. Tarek Sobh

40. Abstract Sensor ModelWe can view the sensory system using three different levels of abstraction • Dumb Sensor: returns raw data without any interpretation. • Intelligent Sensor: interprets the raw data into an event. • Controlling sensor: can issue commands based on the received events.

41. 3 Levels of Abstraction

42. DistributedControl Architecture

43. Trajectory of the robot in a hallway environment

44. Trajectory of the robot from the initial to goal point

45. Trajectory of the robot in the lab environment

46. University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory Discrete Event and Hybrid Systems Prof. Tarek Sobh

47. The ProblemHybrid systems that contain a “mix” of: • Continuous Parameters and Functions. • Discrete Parameters and Functions. • Chaotic Behavior. • Symbolic Aspects. Are hard to define, model, analyze, control, or observe !!

48. Discrete Event Dynamic Systems (DEDS) are dynamic systems (typically asynchronous) in which state transitions are triggered by the occurrence of discrete events in the system. Modified DEDS might be suitable for representing hybrid systems.

49. Eventual GoalDevelop the Ultimate Framework and Tools !! • Controlling and observing co-operating moving agents (robots). • A CMM Controller for sensing tasks. • Multimedia Synchronization. • Intelligent Sensing (for manufacturing, autonomous agents, etc...). • Hardwiring the framework in hardware (with Ganesh).

50. Applications • Networks and Communication Protocols • Manufacturing (sensing, inspection, and assembly) • Economy • Robotics (cooperating agents) • Highway traffic control • Operating systems • Concurrency control • Scheduling • Assembly planning • Real-Time systems • Observation under uncertainty • Distributed Systems