Robotics, Intelligent Sensing and Control Lab (RISC) University of Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC 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 Projects Robotics and Prototyping n 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.) n Service robotics (tire-changing robots) n Web tele-operated control of robotic manipulators (for Distance Learning too). n Algorithms for manipulator workspace generation and visualization in the presence of obstacles.

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

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

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

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

The required sub-systems for robot manipulator prototyping: n Design n Simulation n Control n Monitoring n Hardware selection n CAD/CAM modeling n Part Ordering n 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 Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory

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

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

Why reverse engineering? n Applications: –Legal technicalities. –Unfriendly competition. –Shapes designed off-line. –Post-design changes. –Pre-CAD designs. –Lost or corrupted information. –Isolated working environment. –Medical. n Interesting problem n 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 Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory

Propagation of Uncertainty

Refining Image Motion n Mechanical limitations n Geometrical imitations

Fitting Parabolic Curves

2-D Motion Envelopes

Flow Envelopes

3-D Event Uncertainty

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

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

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

3 Levels o f 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 Bridgeport Department of Computer Science and Engineering Robotics, Intelligent Sensing and Control RISC Laboratory

The Problem Hybrid systems that contain a “mix” of: n Continuous Parameters and Functions. n Discrete Parameters and Functions. n Chaotic Behavior. n 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 Goal Develop the Ultimate Framework and Tools !! n Controlling and observing co-operating moving agents (robots). n A CMM Controller for sensing tasks. n Multimedia Synchronization. n Intelligent Sensing (for manufacturing, autonomous agents, etc...). n Hardwiring the framework in hardware (with Ganesh).

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

Discrete and Hybrid Systems Tool

Other Projects n Modeling and recovering uncertainty in 3-D structure and motion n Dynamics and kinematics generation and analysis for multi-DOF robots n Active observation and control of a moving agent under uncertainty n Automation for genetics application n Manipulator workspace generation in the presence of obstacles n Turbulent flow analysis using sensors within a DES framework

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