Robotics intelligent sensing and control lab risc
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University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory. Robotics, Intelligent Sensing and Control Lab (RISC). Faculty, Staff and Students. Faculty: Prof. Tarek Sobh. Staff:. Lab Manager: Abdelshakour Abuzneid

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Robotics, Intelligent Sensing and Control Lab (RISC)

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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

Robotics, Intelligent Sensing and Control Lab (RISC)


Faculty staff and students

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.


Objectives and ongoing projects robotics and prototyping

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.


Objectives and ongoing projectsrobotics and prototyping

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.


Objectives and ongoing projects robotics and prototyping cont

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.


Objectives and ongoing projects 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.


Objectives and ongoing projects hybrid and autonomous systems

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


Prototyping environment for robot manipulators

University of Bridgeport

Department of Computer Science and Engineering

Robotics, Intelligent Sensing and Control

RISC Laboratory

Prototyping Environment for Robot Manipulators

Prof. Tarek Sobh


To design a robot manipulator the following tasks are required

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.


The required sub systems for robot manipulator prototyping

The required sub-systems for robot manipulator prototyping:

  • Design

  • Simulation

  • Control

  • Monitoring

  • Hardware selection

  • CAD/CAM modeling

  • Part Ordering

  • Physical assembly and testing


Robot prototyping environment

Robot Prototyping Environment


Closed loop control

Closed Loop Control


Pid controller simulator

PID Controller Simulator


Interfacing the robot

Interfacing the Robot


Manipulator workspace generation and visualization in the presence of obstacles

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


Industrial inspection and reverse engineering

University of Bridgeport

Department of Computer Science and Engineering

Robotics, Intelligent Sensing and Control

RISC Laboratory

Industrial Inspection and Reverse Engineering

Prof. Tarek Sobh


What is reverse engineering

What is reverse engineering?

Reconstruction of an object from sensed information.


Why reverse engineering

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.


Closed loop reverse engineering

Closed Loop Reverse Engineering


A framework for intelligent inspection and reverse engineering

A Framework for Intelligent Inspection and Reverse Engineering


Recovering 3 d uncertainties from sensory measurements for robotics applications

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


Propagation of uncertainty

Propagation of Uncertainty


Refining image motion

Refining Image Motion

  • Mechanical limitations

  • Geometrical imitations


Robotics intelligent sensing and control lab risc

Fitting Parabolic Curves


2 d motion envelopes

2-D Motion Envelopes


Flow envelopes

Flow Envelopes


3 d event uncertainty

3-D Event Uncertainty


Tolerancing and other projects

University of Bridgeport

Department of Computer Science and Engineering

Robotics, Intelligent Sensing and Control

RISC Laboratory

Tolerancing and Other Projects

Prof. Tarek Sobh


Problem

Problem

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


Solution

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.


Contribution

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.


Sensing under uncertainty for mobile robots

  • 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


    Abstract sensor model we can view the sensory system using three different levels of abstraction

    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.


    Robotics intelligent sensing and control lab risc

    3 Levels of Abstraction


    Robotics intelligent sensing and control lab risc

    DistributedControl Architecture


    Robotics intelligent sensing and control lab risc

    • Trajectory of the robot in a hallway environment


    Robotics intelligent sensing and control lab risc

    Trajectory of the robot from the initial to goal point


    Robotics intelligent sensing and control lab risc

    Trajectory of the robot in the lab environment


    Discrete event and hybrid systems

    University of Bridgeport

    Department of Computer Science and Engineering

    Robotics, Intelligent Sensing and Control

    RISC Laboratory

    Discrete Event and Hybrid Systems

    Prof. Tarek Sobh


    The problem hybrid systems that contain a mix of

    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 !!


    Robotics intelligent sensing and control lab risc

    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.


    Eventual goal develop the ultimate framework and tools

    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).


    Applications

    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


    Discrete and hybrid systems tool

    Discrete and Hybrid Systems Tool


    Discrete and hybrid systems tool1

    Discrete and Hybrid Systems Tool


    Other projects

    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


    The end

    THE END


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