Robotics, Intelligent Sensing and Control Lab

(RISC)

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

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 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

– 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.)

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 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 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

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

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:

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

Robot Prototyping Environment

Closed Loop Control

PID Controller Simulator

Interfacing the Robot

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

Industrial Inspection and Reverse Engineering

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

What is reverse engineering?Reconstruction of an object

from sensed information.

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

A Framework for Intelligent Inspection and Reverse

Engineering

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

Propagation of Uncertainty

Refining Image Motion

Mechanical limitations Geometrical imitations

Fitting Parabolic Curves

2-D Motion Envelopes

Flow Envelopes

3-D Event Uncertainty

Tolerancing and Other Projects

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

ProblemProblem A unifying framework for

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

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.

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.

Sensing Under Uncertainty for Mobile Robots

Prof. Tarek Sobh

University of Bridgeport Department of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

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.

3 Levels of Abstraction

Distributed Control Architecture

Trajectory of the robot in a hallway environment

Trajectory of the robot from the initial to goal point

Trajectory of the robot in the lab environment

Discrete Event and Hybrid Systems

Prof. Tarek Sobh

University of BridgeportDepartment of Computer Science and Engineering

Robotics, Intelligent Sensing and ControlRISC Laboratory

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 !!

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 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

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

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