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

“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

“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)

“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)

Common Q: What is a Robot?

• No standard answer.

• Perspectives vary

– Children

– Scientists

– Engineers

– Doctors

– Artists

– Movie makers

• Applications vary

– Science

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

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.

Generations of IS

• AI based systems

• Connectionist and Machine Learning

• Agent Based Systems

• Evolutionary Systems

Eras of Robotics Developments

• Industrial Robotics

• Service Robotics

• Personal, humanoid Robotics

• Exploratory and scientific robotics

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

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

Service Robotics

• Health care

• Medical

• Hospitality

• Disaster support

• De-mining applications

Characteristics

• Interdisciplinary programs – ME, EE, CSE

(AI), Bio-medical

• New innovations and IP in sensors,

algorithms

• New definitions of safety, programmability

• Adaptable intelligent behaviour

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

• 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

• 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

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.

Active Gait : Always stable

Passive Gait : Sometimes unstable

Human Evolution vs. Humanoid Evolution

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

• 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

M-TRAN III

PolyBot G3

ATRON

Reconfigurable manipulators in space

Locomotion shapes from modules

ATRON

Exploratory Robotics

• Remotely operated vehicles

• Autonomous vehicles

• Ground vehicles – wheeled, legged, hybrid

• Ariel Vehicles – UAVs,

• Underwater – ROVs, AUVs, UUVs

• Space Robotics

• Inter planetary rovers

Characteristics

• Interdisciplinary and interface to science

• Infrastructure national, global – like GPS,

Gagan etc

• Communications technologies

• Energy management systems

• Autonomous systems and remote /

teleoperations

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

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

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

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.

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.

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

AUV of MoES (IIT Kgp & CMERI)

Ocean going AUV in test at NPOL UARF Kulamavu

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.

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.

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)

Test Bed AUV schematic

Batteries

Onboard Embedded

Computer & control

Propulsion Thruster

Roll damping fins

Attitude control

thrusters

Sonar and Vision

INS and GPS

IIT Kgp AUV Test-bed

Hardware assembled in the AUV for motion control and navigation

• 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

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

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

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

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

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

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

GPS plots using two receivers

X (meters)

Y

Y

X (meters)

Google earth plot

Google earth plot

X (meters)

X (meters)

y

y

• 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

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

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

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

Accelerometers

Coordinate system Transformation

Gravity, Non-gravitational

AccelerationCorrection

Gyros

Initial Alignment

Attitude computing

IntegrationAcceleration Integration

INPUT

navigation phase

OUTPUT

alignment phase

Position

Velocity

Attitude

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

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

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

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

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

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

Retraining of Inverse Model (Online control)

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

02/13/11

Motion in X-Y Plane

Motion in Y-Z Plane

Swimming pool trial Videos…

Thank you for your kind attention l,

Map of tank from sonar

• Distance and bearing maps constructed of test

tank

• Obstacle position determination codes ready.

What is in it for you?

• Several aspects of Engineering

– Mechanical

– Electrical

– Electronics

– Computer Science

• Mechatronics

• Application Engineering

– OceanEngineering

– Aerospace Engineering

– Science studies

Exciting Possibilities

Teaching and Research

Intelligent Systems

Mechanisms

Control Systems

Automation

Cybernetics

Medicine – surgery, rehabilitation etc.

Questions

Email: kumar@mech.iitkgp.ernet.in

Also visit an experience site in robotics labs

vlabs.co.in

vlabs.iitkgp.ernet.in

(request us for your login / access)