Thruster based AUV Test Bed · Unmanned Underwater Vehicle… 2) Autonomous Underwater Vehicles...
Transcript of Thruster based AUV Test Bed · Unmanned Underwater Vehicle… 2) Autonomous Underwater Vehicles...
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Robotics
Talk to students of RGUKT
Robotics and Intelligent Systems Laboratory
& CAD-CAM Laboratory
C. S. Kumar
Department of Mechanical Engineering
Indian Institute of Technology Kharagpur
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“Robotics is an exciting science. The best of
the brains in science and engineering are
doing interesting activities in robotics.
There is a lot of future to look ahead to …”
– Bill Gates in Science 2007
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“India is expected to see a lot of Robotics
and its applications in 2009 in areas of
Autonomous Vehicles, Intelligent robots
etc. through applications in Space,
Defence and science…”
– Hon’ble PM Dr Manmohan Singh
(Comments on the preparation for India Robotics
2009)
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“India is expected to see a lot of Robotics
and its applications in 2009 in areas of
Autonomous Vehicles, Intelligent robots
etc. through applications in Space,
Defence and science…”
– Hon’ble PM Dr Manmohan Singh
(Comments on the preparation for India Robotics
2009)
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Common Q: What is a Robot?
• No standard answer.
• Perspectives vary
– Children
– Scientists
– Engineers
– Doctors
– Artists
– Movie makers
• Applications vary
– Science
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Key aspects
• Machine capable to do various actions on
its own
• Replicate humans or human activity
• Programmable, repetitive and precise
• Intelligence
• Interact
• Respond and react to various inputs /
situations
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What is Intelligence?
The ability of the system to act appropriately in an
uncertain environment, where appropriate action is
that which increases the probability of success, and
success is the achievement of behaviors sub-goals
that support the systems ultimate goal"
J.S.Albus
Criteria of success and ultimate goal defined external
to the I.S.
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Generations of IS
• AI based systems
• Connectionist and Machine Learning
• Agent Based Systems
• Evolutionary Systems
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Eras of Robotics Developments
• Industrial Robotics
• Service Robotics
• Personal, humanoid Robotics
• Exploratory and scientific robotics
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Industrial Robotics
• Automation
• Precision
• Manufacturing Productivity through
high-rate processes
• Reliable, failsafe operations
• Calibration required for applications
• Rapid Programming and deploy ability
• Several developments in tooling and
applications
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Characteristics
• Academic Programs in mechatronics,
CAD-CAM, CIM etc
• R&D in sensors, actuators, power control
• Miniaturization as well as very large scale
automation (e.g., mining)
• Internet and web based control
• Industry driven – lower costs, high
reliability, ease of deployment
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Service Robotics
• Health care
• Medical
• Hospitality
• Disaster support
• De-mining applications
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Characteristics
• Interdisciplinary programs – ME, EE, CSE
(AI), Bio-medical
• New innovations and IP in sensors,
algorithms
• New definitions of safety, programmability
• Adaptable intelligent behaviour
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Personal / Humanoid Robots
• Home use / to assist people / paly toys
• Human-like
• Natural / animal like
• Sophisticated science fiction like
• Considerable research in motion and
control
• Considerable popularity for home and
children
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• Bipedal human-like locomotion
– Stable gait
• Changing model during one/two feet support
walking
• Two legs, two arms, head, torso
• Hyper DOF system (>20)
– Complex kinematics and dynamics
• Complex real-time control architecture
Challenges in Humanoids
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• Bipedal human-like locomotion
– Stable gait
• Changing model during one/two feet support
walking
• Two legs, two arms, head, torso
• Hyper DOF system (>20)
– Complex kinematics and dynamics
• Complex real-time control architecture
Challenges in Humanoids
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ZMP (Zero Moment Point) specifies the point with respect to which dynamic reaction force at the contact of the foot with the ground does not produce any moment, i.e. the point where total inertia force equals 0 (zero).
ZMP is the indicator ofthe stability of the robot: if it is in the foot shadow –
stable, İf not – unstable.
The shadow depends on single or double support phase.
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Active Gait : Always stable
Passive Gait : Sometimes unstable
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Human Evolution vs. Humanoid Evolution
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Self reconfigurable and modular
robots• collections of modules
– each module is a robot
• self-reconfigurable
– modules can change connections
– so overall robot changes shape
• “modular self-reconfigurable” robots adjust
shape to task
• Purpose : locomotion
– wheel, spider, snake, …
• Purpose : manipulation
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• Autonomous kinematic machines with
variable morphology.
• Robots with one or more basic unit or
module
• Ability to deliberately change their own
shape by rearranging the connectivity of
their modules
• Converts into any desired shape and
change its functionality without external
help
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M-TRAN III
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PolyBot G3
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ATRON
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Reconfigurable manipulators in space
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Locomotion shapes from modules
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ATRON
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Exploratory Robotics
• Remotely operated vehicles
• Autonomous vehicles
• Ground vehicles – wheeled, legged, hybrid
• Ariel Vehicles – UAVs,
• Underwater – ROVs, AUVs, UUVs
• Space Robotics
• Inter planetary rovers
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Characteristics
• Interdisciplinary and interface to science
• Infrastructure national, global – like GPS,
Gagan etc
• Communications technologies
• Energy management systems
• Autonomous systems and remote /
teleoperations
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India’s Ocean Program
• Earth is more than 60 % water (oceans)
• Resource potential is largely unexplored
• Nation has a vast ocean exclusive
economic zone – 370 km of the coast with
an area of 1,641,514 sq. Km
• Major potential for many future minerals,
energy resources and drugs for mankind
• Exploration, adventure and security
• Ministry of Earth Sciences – Department
of Ocean Development
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Major national initiatives
• Scientific study of ocean from space and surface
vessels
• Observations, climatic influence etc.
• Organizations : National Institute of Ocean
technologies (NIOT), National Centre for
Antarctic and Oceanic Research (NCAOR),
Indian National Centre for Ocean Information
Systems (INCOIS)
• Robotic Programmes: Remotely Operated
Vehicles, Autonomous Underwater Vehicles
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Autonomous Underwater
Vehicles• No cable and external power
• Internal energy sources: Batteries, fuel cells etc.
• Low power consumption
• Needs autonomy due to poor communication
• Mainly for observation, surveying etc
• Unmanned Undersea vehicles
• Autonomous Underwater Vehicles
• Sea Gliders
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Unmanned Underwater Vehicle…
1) Remotely Operated Vehicles (ROVs) –
► Tele-operated through cable.
► From which power is supplied.
► Also remotely operated.
► With increase in depth of operation the hydrodynamic drag &
power transmission losses increases.
► So to remove these dependencies a degree of autonomy is
required.
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Unmanned Underwater Vehicle…
2) Autonomous Underwater Vehicles (AUVs) –
► Is a robotic device.
► Driven by propulsion system, controlled and piloted by onboard
computer, and also maneuverable in three dimensions.
► Designed to carry out its specific mission in a predetermined time
frame.
► They are intended mainly to carry payloads ie. sensors or
instruments that are capable of activities like…
- hydrographic mapping,
- ocean weather forecasting,
- oceanographic data logging,
- military operations,
- sea life surveying.
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Indian AUV
• 1st vehicle to explore upto 150 m deep
• Next phase 600 m depth
• Finally at 3000m / 6000 m depth
• Initial Proof of concept has been initiated
• AUV by IIT Kharagpur along with CMERI
Durgapur for use by NIOT, Chennai
• 1st Prototype test bed at IIT Kharagpur is ready
and operational
• A prototype is readied at CMERI for trials
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AUV of MoES (IIT Kgp & CMERI)
Ocean going AUV in test at NPOL UARF Kulamavu
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AUV Test bed
• IIT Kgp has a laboratory test bed AUV for its R&D in AUV technology
• Navigation and motion control of AUV in real seas requires complex control schemes
• Significant R&D on motion control schemes need to be confirmed on test bed before launching sea operations
• Test Bed has been designed to prototype and verify control schemes for– AUV motion control
– AUV motion tracking observer
– Sonar and vision based obstacle and landmark sensing
– Autonomous control scheme development.
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AUV Challenges requiring tests
• Very poor / week communication with ship
• Autonomous control instead of remote control
• Variable and low precision in gyros and accelerometers
• Significant R&D on the control schemes need to be confirmed on test bed before launching real operations
• Test Bed has been designed to prototype and verify control schemes for– AUV motion control
– AUV motion tracking observer
– Sonar and vision based obstacle and landmark sensing
– Autonomous control scheme development.
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Specs of the AUV Navigation and
Control Test bed
• Length = 1.9 meters
• Diameter = 260 mm
• Dry weight = 76 Kgs (nominal without payload)
• Buoyancy = 76.5 Kgs
• 1 propulsion thruster + 4 control thrusters
• Lead acid batteries based power system
• Power capacity = 850 WHr approx. (upgraded to Li-Ion)
• SS hull with real-time sensing and control for experimental payload and development platform (support for different sensors)
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Test Bed AUV schematic
Batteries
Onboard Embedded
Computer & control
Propulsion Thruster
Roll damping fins
Attitude control
thrusters
Sonar and Vision
INS and GPS
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IIT Kgp AUV Test-bed
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Hardware assembled in the AUV for motion control and navigation
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• Inertial Navigation System
• System state estimator
• Trajectory control
• Real Time OS – RT-Linux
• AUV housekeeping
• Auxiliary systems
• External communications
• GPS interface
• Wireless management
• Trajectory and Path Plans
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Motion Control in AUV
Heave / Pitch
Sway / Yaw
Surge X
Y
Z
• High maneuverability
• Low speed attitude and direction control feasible
• Non-linearities in hydrodynamic coefficients
o velocity and shape (geometry) dependant terms
o configuration (state) dependant terms
• Coupled motions
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Motion Control Architecture for AUV
Multi-axes coordinated
thruster control
Commanded trajectory
from motion planner
INS with
Kalman Filter
estimator
GPSLBL/USBL
DVL
Dead reckoning
IMU+accelerometers
Intelligent motion planner and collision
avoidance
To thruster
controllers
Vehicle Model based
dynamics and control
Communications
interface
Estimated
positions
Communication
to/from ship
Vehicle
Sonar data
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Control and Sensors used
• Proportional-derivative
• Depth sensor – 1 centimeter accurate
• Three-axis inclinometer – 0.2 deg in roll and pitch, 0.3
deg in yaw
• Three-axis fiber-optic gyro – 20 deg/hr bias stability
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Experiments on Attitude Control
Fig. Depth regulation
Kpz =150, Kpth = 2.5, Kpps = 0.6 Kdz =100, Kdth = 2, Kdps = 0.2
Fig. Pitch regulation
Fig. Pitch regulation
Fig. Depth tracking Fig. Pitch regulation during tracking
Fig. Heading regulation
Kpps = 1.5 Kdps = 1.0
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Motion Tracking Observer
• Motion Sensors– XSENS MT9 : 3-axes Piezo Gyro, 3- Axes
Accelerometer and 3 Axes Magnetometer
– XBOW Fiber Optic Gyro (6 DOF) as Attitude Heading Reference System
– PC-104 based Rockwell 12-channel GPS
– Altimeter (Tritec) and Depth Sensor (PNI)
• Internal INS and data fusion system
• Parametrically scalable for ocean going AUV
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GPS based navigation for AUV
Tested scenario in laboratory: 2D vehicle
Assumption - using 2D model for land based vehicle tracking
Use of Known tracks from maps / satellite imagery (Google Earth)
Comparison of GPS data logging and computation of navigation
paths
Error Analysis
Map and scale navigation application to AUV
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GPS plots using two receivers
X (meters)
Y
Y
X (meters)
Google earth plot
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Google earth plot
X (meters)
X (meters)
y
y
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• Position pinning – on
• Position and velocity smoothing - off
• Position pinning – off
• Position and velocity smoothing - on
• Position pinning – off
• Position and velocity smoothing - off
x
y
x
y
x
y
X axis – x meters
Y axis – y meters
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Vehicle motion track on land using GPS
• Motion tracking updates from GPS
• Compared with Google Earth data
• Sea trials will use long baseline /
short base line trilaterization with
acoustic systems
Google earth track in campus
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GPS/INS Integration
High position and velocity accuracy
Precise attitude determination
High data rate
Navigational output during GPS signal outages
Cycle slip detection and correction
Gravity vector determination
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GPS data Computer fix
p,q,r
ax,ay,az
VN
VE
VD
Positionx,y,z
∫dt
Initial states
Assimilation for initial states
+
ge
Euler angles
Low freq update
DCMVi
u,v,w
INS loop at 100 Hz
GPS/INS Integration on AUV
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Accelerometers
Coordinate system Transformation
Gravity, Non-gravitational
AccelerationCorrection
Gyros
Initial Alignment
Attitude computing
IntegrationAcceleration Integration
INPUT
navigation phase
OUTPUT
alignment phase
Position
Velocity
Attitude
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Experimental Results…full Trajectory
370 to 410 sec
320 to 370 sec
190 to 230 sec
140 to 190 sec
230 to 280 sec
280 to 320 sec
st line
st line
st line
90° turn
90° turn
90° turn
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Experimental Results…full Trajectory
370 to 410 sec
320 to 370 sec
190 to 230 sec
140 to 190 sec
230 to 280 sec
280 to 320 sec
st line
st line
st line
90° turn
90° turn
90° turn
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Recurrent neural network based control
• Models A Dynamical System
• Combines back-propagation features with temporal behavior of states
• Very effective for non-linear dynamics in plant (AUV)
• Easily trainable to input output data from simulations as well as real-plant
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Forward Model of AUV
Heave / Pitch
Sway / Yaw
Surge X
Z
• Control Inputs:
– Forces and Torques
– AUV motion outputs
• Trained for trajectories
– Straight Line
– Circular interpolated
curves
• Experimented with
simulation data for
various time steps and
step size
• Variation of learning
rate and MSE
considered
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Inverse Model (control)
• Captures the controller
dynamics of the AUV
• Inputs as motions (state) of
AUV
• Outputs as forces and torques
• Several training sets used to
increase generalization
capability
• Experimented with learning
rate parameter and MSE
• Very low MSE used (1E-7)
Heave /
Pitch
Sway /
Yaw
Surg
e
X
Z
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Online Learning Neuro-Controller
Proposition: Plant as additional Layer,
Instantaneous sensitivity,
Pass through plant,
Low learning rate
Linear PositionsLinear Positions
Angular PositionsPlant
AUV Model
Neuro-controller
Angular Positions
Desired
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Retraining of Inverse Model (Online control)
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Data used for Online Control
Ramp input forces in X, Y, Z directions.
Linear forces varying with time are given for Tx, Ty, Tz
• X-Y Plane Motion: Fx, Fy and Tz
• Y-Z Plane Motion: Fy, Fz andTx
• Z-X Plane Motion. Fz, Fx and Ty
To illustrate the online mechanism the value of theramp force had been changed after sometime, andhence the online control comes into play where ittries to bring the Vehicle to the Normal Path
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02/13/11
Motion in X-Y Plane
Motion in Y-Z Plane
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Swimming pool trial Videos…
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Thank you for your kind attention l,
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Map of tank from sonar
• Distance and bearing maps constructed of test
tank
• Obstacle position determination codes ready.
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What is in it for you?
• Several aspects of Engineering
– Mechanical
– Electrical
– Electronics
– Computer Science
• Mechatronics
• Application Engineering
– OceanEngineering
– Aerospace Engineering
– Science studies
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Exciting Possibilities
Teaching and Research
Intelligent Systems
Mechanisms
Control Systems
Automation
Cybernetics
Medicine – surgery, rehabilitation etc.
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Questions
Email: [email protected]
Also visit an experience site in robotics labs
vlabs.co.in
vlabs.iitkgp.ernet.in
(request us for your login / access)