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

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

Robotics, Intelligent Sensing and Control Lab

(RISC)

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 2: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 3: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 4: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 5: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 6: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 7: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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

Page 8: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Prototyping Environment for Robot Manipulators

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 9: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 10: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

The required sub-systems for robot manipulator prototyping:

Design Simulation Control Monitoring Hardware selection CAD/CAM modeling Part Ordering Physical assembly and testing

Page 11: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Robot Prototyping Environment

Page 12: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Closed Loop Control

Page 13: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

PID Controller Simulator

Page 14: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Interfacing the Robot

Page 15: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Manipulator Workspace Generation and Visualization in the Presence of Obstacles

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 16: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 17: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 18: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 19: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 20: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Industrial Inspection and Reverse Engineering

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 21: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

What is reverse engineering?Reconstruction of an object

from sensed information.

Page 22: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 23: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Closed Loop Reverse Engineering

Page 24: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

A Framework for Intelligent Inspection and Reverse

Engineering

Page 25: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Recovering 3-D Uncertainties from Sensory Measurements for

Robotics Applications

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 26: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Propagation of Uncertainty

Page 27: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Refining Image Motion

Mechanical limitations Geometrical imitations

Page 28: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Fitting Parabolic Curves

Page 29: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

2-D Motion Envelopes

Page 30: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Flow Envelopes

Page 31: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

3-D Event Uncertainty

Page 32: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Tolerancing and Other Projects

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 33: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

ProblemProblem A unifying framework for

tolerance specification, synthesis, and analysis across the domains of industrial inspection using sensed data, CAD design, and manufacturing.

Page 34: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

SolutionSolution 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.

Page 35: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

ContributionContribution

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.

Page 36: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 37: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 38: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing
Page 39: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Sensing Under Uncertainty for Mobile Robots

Prof. Tarek Sobh

University of Bridgeport Department of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 40: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 41: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

3 Levels of Abstraction

Page 42: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Distributed Control Architecture

Page 43: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Trajectory of the robot in a hallway environment

Page 44: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Trajectory of the robot from the initial to goal point

Page 45: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Trajectory of the robot in the lab environment

Page 46: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Discrete Event and Hybrid Systems

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

Page 47: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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 !!

Page 48: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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.

Page 49: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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).

Page 50: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

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

Page 51: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Discrete and Hybrid Systems Tool

Page 52: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Discrete and Hybrid Systems Tool

Page 53: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

Other Projects Modeling and recovering uncertainty in 3-D

structure and motion Dynamics and kinematics generation and analysis

for multi-DOF robots Active observation and control of a moving agent

under uncertainty Automation for genetics application Manipulator workspace generation in the presence

of obstacles Turbulent flow analysis using sensors within a

DES framework

Page 54: Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing

THE END