Title: Robotics, Intelligent Sensing and Control Lab RISC
1Robotics, Intelligent Sensing and Control Lab
(RISC)
University of Bridgeport School of Engineering
Tarek SobhVice president for graduate studies
and research Dean, School of Engineering
2Outline of Outgoing Project
- Online Automation and Control An Experiment in
Distance Engineering Education - E-Learning Case Studies in Web-Controlled
Devices and Remote Manipulation - Prototyping Environment for Robot Manipulators
- Manipulator Workspace Generation and
Visualization in the Presence of Obstacles - Kinematic Synthesis of Robotic Manipulators from
Task Descriptions - New concept in optimizing the manipulability
index of serial Manipulators using SVD method
3Outline of Outgoing Project
- Industrial Inspection and Reverse Engineering
- Recovering 3-D Uncertainties from Sensory
Measurements for Robotics Applications - Sensing Under Uncertainty for Mobile Robots
- Service RobotsA Tire Changing Manipulator
- Robot Design and Dynamic Control Simulation
Software Solutions From Task Points Description - Experimental Robot Musicians
- Design and Implementation of a Multi-sensor
Mobile Platform
4Online Automation and Control An Experiment in
Distance Engineering Education
5Introduction
- Online Distance Education is a major part of the
current education system - Started as an internal exercise to share and
discuss ideas - Ever growing need for part-time education
- 213 Universities offering online courses at
various levels and disciplines in the US - Majority of the online courses are non-technical
- Lacking laboratory based courses
6Need for Online Education
- Part time course work
- Working class willing to pursue higher education
- Social responsibilities
- Current socio-political situation
- National and International demand
7Distance Engineering Education
- Accredited engineering degrees
- Under-graduate and Graduate level
- Computer Engg, Electrical Engg, Mechanical Engg
- Comprehensive laboratory based courses.
8Partnerships
- Great value of American engg. degrees overseas
- Partnership with foreign University/Institution
providing - Infrastructure
- Teaching support
- Examination facilities
- Closer to the student concentration
- Helps in better delivery of courses
9Projects Implemented Towards DL Education
- Mobile Robot Controlled by a Phone
- Internet Based Software Library for the SIR-1
Serial Port Controlled Robot - Internet Based Computer Vision Framework For
Security, Surveillance And Tracking Applications
10Online Distance Laboratories
- Using Automation and Telerobotic (controlling
devices from a distance) systems - Real-time laboratory experience via the internet
- Tele-operation of Mitsubishi Movemaster
- RISCBOT A Web Enabled Autonomous Navigational
Robot - Tele-operation of the FESTO Process Controller
111. Tele-operation of Movemaster
- Can be used in 3 modes
- Evaluation mode
- Teacher mode
- Student mode
12RISCBOT
- Waits for command from the server.
- Wall clinging robot.
- Image processing program checks for doors.
- Uses Ultrasonic sensors for obstacle avoidance.
- PC acts as central decision maker.
13RISCBOT IN ACTION
14RISCBOT CONTROL WEBSITE
15FESTO Process Controller
- Providing telerobotic operability of the FESTO
process control machine by interfacing it with
the Mitsubishi Movemaster robot.
16Conclusion
- Virtual online collaboration
- Lab-based distance education
- Accredited Engineering/Technical lab-based
experience, degrees training via distance
learning
17E-Learning Case Studies in Web-Controlled
Devices and Remote Manipulation
18Mobile Robot Controlled by a Phone
An application of a Robot with a phonechip
19Mobile Robot Controlled by a Phone
PHONEBOT Basic Block Diagram
20Internet Based Software Library for the SIR-1
Serial Port Controlled Robot
- Web Based Control / Remote Automation
- API functions for SIR-1 Remote Manipulation
direct / inverse kinematics, multiple
simultaneous serial-port-communication
interfacing, link speed control
21Internet Based Software Library for the SIR-1
Serial Port Controlled Robot
Internet
22Internet Based Computer Vision Framework For
Security, Surveillance And Tracking Applications
- Vision Framework for Real-Time Tasks with
Off-The-Shelf Hardware - Early processing (Gaussian Filters, Histogram
Normalization, Color Filtering) - Feature Extraction (Edge Detection, Line /
Ellipse detection, Region Growing, Region
Splitting, MinMax point extraction) - Feature Matching
23Internet Based Computer Vision Framework For
Security, Surveillance And Tracking Applications
acquisition
color filtering
conversion to monochrome
Gaussian blur
thresholding
MinMax feature extractor
heuristic feature detection
feature matcher
match result
24Prototyping Environment for Robot Manipulators
25Robot Prototyping Environment
26Design Parameters Subsystem Notification
27Database Design Considerations
28To 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.
29Web Enabled Robot Design and Dynamic Control
Simulation Software Solutions From Task Points
Description
30Research Summary
- A web-based solution for robot design and dynamic
control simulation based on given task point
descriptions - The software combines and utilizes the
computational power of both the Mathematica and
Matlab packages
31Research Summary (cont.)
- Given the location and velocity of each task
point, our approach formulates the complete
design of a 3 DOF robot model by computing its
optimal dynamic parameters such as link length,
mass and inertia - Suggests the optimal control parameters (Kp, Kv)
for the dynamic control simulation
Puma560 3 DOF robot
32The Software Package
- Web Interface
- Kinematic Design Module
- Dynamic Design Module
- Dynamic Control Simulation Module
33The Software Package (Cont.)
34Web Interface
- JSP, Servlet, JLink and JMatservlet
- Central control module
35Results User login Screen
sample run video
36User specifies number of task points
37User specifies the coordinates and velocities of
each task points with respect to a time scale
38User specifies link radii for dynamic model
generation, and Kp, Kv initialization for dynamic
PD control simulation
39DH table, Dynamic Parameter Matrix and optimal
Kp, Kv values for each link
40A standard PPP model
41Desired Trajectory for link 1, 2, 3
Desired Vs. obtained link displacement for link 1
42Desired Vs. obtained link displacement for link 2
Desired Vs. obtained link displacement for link 3
43Desired velocity trajectory for link 1, 2 and 3
Desired Vs. Obtained velocity for link 1
44Desired Vs. Obtained velocity for link 2
Desired Vs. Obtained velocity for link 3
45Ergonomic and Efficient Software
Alternatives for High Cost Manipulators - Direct,
Wireless and Networked Control Techniques
46High Cost Manipulators
- deciding-on and purchasing the right
manipulator(s) for a predetermined task (budget,
purchasing time) - educational institutions (diversity of software /
hardware controlling techniques possibility of
becoming victims of abusive usage)
47Ergonomic and Efficient Software Alternative
- software simulation and control package
- standalone simulator
- networked simulator
- virtual manipulator
- remote automation / distance learning
- cell phone based control
48The Manipulator Used in the Implementation
- Mitsubishi, RV-M1 (Movemaster EX)
- general purpose commercial arm
- 5 DOF
49The Simulator Kinematics
IK/DK control, workspace-safe, real-time,
CAD/robot
50The Simulator Trajectory Control
real-time trajectory modeling and testing,
CAD/robot
51The Simulator Networking Model
client simulator
client/server simulator
client/server simulator
server/robot simulator
- direct serial link connectivity
- pipelined TCP/IP connectivity, allowing for
effective distance learning methods and flexible
remote automation and control
actual robot
52The Simulator Networking Model Scenario
controlling the robot through 2 pipelined
simulators
53The Simulator Cell Phone Server Mode
in cell phone server mode, the simulator allows
direct control over the manipulator or a pipeline
of simulators, through a web enabled cell phone
54Manipulator Workspace Generation and
Visualization in the Presence of Obstacles
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59Kinematic Synthesis of Robotic Manipulators from
Task Descriptions
60Envisioning Optimal Geometry
61Objectives
- Parameters considered in this work
- Coordinates of the task-points
- Spatial constraints
- Restrictions (if any) on the types of joints
- Goals
- Simplified interface
- Performance
- Modular architecture to enable additional
optimization modules (for velocity, obstacles,
etc.)
62Manipulability Measure
wvdet(JJT)
- For performance purposes the manipulability
measure was the one originally proposed by Tsuneo
Yoshikawa - Singular configurations are avoided by maximizing
the determinant of the Jacobian matrix
63Optimization Measure
64Screenshots
65Sample I Trajectory
66Sample I Manipulability Ellipsoids
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69Sample II Manipulability Ellipsoids
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72New concept in optimizing the manipulability
index of serial manipulators using the SVD method
73- Studying the manipulability index for every point
within the workspace of any serial manipulator is
considered one of the important issues, required
for designing trajectories or avoiding singular
configurations. - The manipulability measure is an indicator of how
close the manipulator is to being in singular
configurations .
74Manipulability Bands of six degrees of freedom
manipulator
75Manipulability Bands of Puma 560 in 2-D workspace
76Manipulability Bands of Mitsubishi movemaster in
2-D workspace.
77Industrial Inspection and Reverse Engineering
78Why 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.
79Closed Loop Reverse Engineering
80A Framework for Intelligent Inspection and
Reverse Engineering
81Recovering 3-D Uncertainties from Sensory
Measurements for Robotics Applications
82Propagation of Uncertainty
83Flow Envelopes
84Tolerancing and Other Projects
85Problem
A unifying framework for tolerance
specification, synthesis, and analysis across the
domains of industrial inspection using sensed
data, CAD design, and manufacturing.
86Solution
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 tool paths of the sensed part.
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88Sensing Under Uncertainty for Mobile Robots
89Abstract 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.
90Trajectory of the robot in a hallway environment
91Trajectory of the robot in the lab environment
92Potpourri of other RA Projects
93Projects
- 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
94Service RobotsA Tire Changing Manipulator
95Design and Construction
- A prototype of the racing car
96Design and Construction
- The manipulator will be of the depicted form. The
design was derived from inertial and dexterity
calculations - Three essential Components the sliding
mechanism, the arm, and the end effector system.
97Design and Construction
- All of the four arms should be suspended with the
visualized sliding mechanism.
98Complete Design Schema
customer
manipulator based manipulator manufacturing
web based interface
simulation based quality assurance and testing
data acquisition
prototyping environment
packaging and shipping
99Experimental Robot Musicians
100Introduction
- Robot musicians perform on real instruments
through the usage of mechanical devices, such as
servomotors and solenoids - Research innovations linking music, robotics and
computer science
101MotivationMusic Expressiveness
- Offer the audience live-experience very similar
to listening to a human musician. - Real instrument performance, such as the physical
vibration of a violin string, provides a much
stronger case in music expressiveness, versus
electronic music synthesizers. - Mozart - eine kleine nacht musik whole ensemble
102MotivationMusic Expressiveness (cont.)
- Bypass several technical difficulties that are
typically encountered by human musicians - More degrees of freedom in real-time performances
and reach a higher level of performance
difficulty, flexibility and quality. - As an example, a violin is played by a robot
musician with hands that have 12 fingers.
103Robot Musicians ArchitectureRobot Musicians Band
Overview (cont.)
- Robot musicians, the P.A.M. band, invented by
Prof. Kurt Coble.
The moth features violin solo, composed by Prof.
Kurt Coble, companied by percussion ensemble,
electric base and electric guitar
104Robot Musicians ArchitectureRobot Musicians Band
Overview (cont.)
Austin plays a Percussion Ensemble
Dusty plays a red electric guitar
105Robot Musicians ArchitectureRobot Musician
Architecture Overview
- A three-module architecture
106Robot Musicians ArchitectureMotion Module (Cont.)
Servo attached to one bow of Jasche
Solenoid (with holding power of 1.5 pounds)
attached to Jasche
107Servomotor In Action
108Motion Module In Action
- Mozart - eine kleine nacht musik whole ensemble
109Jasche In Action
Amazing grace traditional American folk song
110Real Time PerformanceMechanical Issues
111Results drum set in action
112Robot Musicians ArchitectureMotion Module (Cont.)
A coffee containers plastic lid is connected
with a servo so it flutters against the body of a
drum when the servo receives control command from
the control module
Sample Motion Module Architecture drumstick
controlled by solenoid
113Robot Musicians ArchitectureMotion Module (Cont.)
Sample Motion Module Architecture chimes wand
controlled by servo
114Design and Implementation of a Multi-sensor
Mobile PlatformRISCBot II
115Sensor Fusion
- Sensor fusion is a method for conveniently
combining and integrating data derived from
sensory information provided by various and
disparate sensors, in order to obtain the best
estimate for a dynamic systems states and
produce a more reliable description of the
environment than any sensor individually.
116Sensor Fusion Categories
- Complementary sensors consist of sensors with
different modalities, such as a combination of a
laser sensor and a digital camera. - Competing sensors are composed of sensors suit
which have the same modality, such as two digital
cameras which provide photographic images of the
same building from two different viewpoints.
117Data Acquisition
- we used a data acquisition module called Data
Translation DT9814 - Advantages
- Low cost USB data acquisition module.
- 24 analog inputs, 2 analog outputs, and one
32-bit counter timer . - Analog signal range of /- 10V .
- Resolution of 12 bits for both the analog input
and analog output subsystems, and input
throughputs up to 50 kHz.
118Sonar Sensor
- we used LV MaxSonar EZ0 ultrasonic sensors
119MaxSonar -EZ0 Sensors
- They are low cost sonar ranger actually
consisting of two parts - An emitter, which produces a 42kHz sound wave
- A detector, which detects 42kHz sound waves and
sends an electrical signal back to the
microcontroller. - Readings can occur up to every 50 ms, (20-Hz
rate) and designed for indoor environments. - Advantage
- They can detect obstacles with high confidence
especially when the object is well defined
120Infrared Proximity Sensor
- Infrared sensors operate by emitting an infrared
light, and detecting any reflection off surfaces
in front of the robot. If the reflected infrared
is detected, it means that an object is detected. - We have used an infrared proximity - Sharp
GP20A21YK
121Infrared Proximity Sensor
122Jazzy 1122 Wheelchair
123Jazzy 1122 Wheelchair
124Navigation and Obstacle Avoidance
- The obstacle may be defined as any object that
appears along the mobile robots. - In the navigation problem, the requirement is to
know the positions of the mobile robot and a map
of the environment (or an estimated map). - The ability of the robot to act based on its
knowledge and sensor values so as to reach its
goal positions as efficiently and as reliably as
possible.
125IMPLEMENTATION AND RESULTS
126RISCbot II
- Reverse engineering process.
- Different types of sensors
- LV MaxSonar- EZ0 ultrasonic
- Sharp GP20A21YK infrared proximity sensors
127RISCbot II
128RISCbot II
129Features
- RISCBot II is a high-payload platform with a
payload up to 350 lbs. -
- RISCBot II is a high-speed platform which moves
up to 6 mph . - RISCBot II powerful motors and two 14 pneumatic
wheels on steel frame with suspension is designed
for higher speeds with good response.
130Features
- RISCBot II is equipped with Active-Trac
Suspension (ATS). - ATS makes the platform to traverse different
types of terrain and obstacles while maintaining
smooth operation . - RISCBot II has two front Anti-Tip Wheels which
work with the ATS to maneuver over obstacles . - RISCBot II also has Rear Casters wheels to
respond to the weight transfer and to pivot while
driving over obstacles.
131CT Post
132CT Post
133Research Collaborators
- Raul Mihali.
- Anatoli Sachenko.
- Sarosh Patel.
- Bei Wang.
- Puneet Batra.
- Amit Singh.
- Sudip Pathak.
- Tomas Vitulskis.
- Andrew Rosca.
- Ayssam El Kady.
- Eslam M. Gebriel.
- Mohammed Mohammed.
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