Introduction to Autonomous RobotsAutonomous Underwater Vehicles
asok@iitm.ac.in
1. Autonomous Underwater Robots: Design, Modelling, and Control
2. Dynamic station keeping control of ROV 3. Multipoint Potential Field method for
obstacle avoidance
5. Surgical Robotics- Design and Development of Tele-surgical trainer robot (Filed 2 patents)
6. Tremor compensation in surgical robotics 7. Aerial Robotics: Design of Novel quadrotor-
VOOPS (Filed 2 patents) 8. Underwater mechatronic system for
selective deployment (Filed 1 patent) 9. Task allocation in multirobot systems
Product Design and Development
1. Limb Immobilisation splint for trauma victims Patented; Licensed to and commercialised by M/s HLL Life care (Asokan, Rahul Ribeiro, Pulin, Darshan) (first product to be commercialized out of the Stanford-India Biodesign program)
2. Combinational Scissor-Grasper for laparoscopic surgery (Asokan, Chinmay Deodhar) Patented and Licensed to M/s Intuitive Surgicals,
USA
3. Wound cleaning Robot for hospital application
4. Non-Destructive Method to Identify Used Syringes (Patent filed in 2012; innovation award from M/s Intellectual ventures; JC Bose Patent award
2013)
5. A device for descaling the inner wall of a tank (Patent filed in 2008)
6. Fetal Growth Monitor for Rural Application
7. Proportional solenoid for space applications (ISRO)
Acknowledgements
Some of the works presented are from the published works of research students. The contributions by the following research scholars/ organisations are thankfully acknowledged:
Dr M Santhakumar
Dr T Periasamy
Dr S Saravanaklumar
inspection New installation of underwater pipeline Periodic checking required of installed
pipeline Task is performed under structured
conditions Extended duration of surveys
up to weeks for extensive pipelines boredom and fatigue
Major Players:
Industry:
Human Occupied Vehicle (HOV)
3/17/2016 10
AUV
Hybrid URV can switch to direct tele- operation mode for complex tasks
Hybrid URV can switch to autonomous mode for simple tasks
Comparison of Task Complexity
Remotely Operated Vehicle (ROV) Tethered Supervised Vehicle:
The vehicle is connected to a mother ship by a cable through which communications, data transmissions and power supply are carried out.
•Restricted operating range
•Operator fatigue affects mission results
•Tether management is the main challenge
• Operationally efficient compared to other class of vehicles
ROV Deployment and Applications
Sub-sea Installations
Autonomous Underwater Vehicle (AUV)
It is a robotic device that is driven through the water by a propulsion system, controlled and piloted by an onboard computer and maneuverable in three dimensions.
It needs to be Pre-programmed
Degree of human intervention will be a function of communication capability
AUV will require fool-proof navigation, control and guidance systems on board to meet the mission accuracy requirements
Transmission of data back to mother ship if on-board data storage with post mission retrieval does not meet the mission requirements
Autonomous Underwater Vehicle (AUV)
monitoring. Tsunami detection. Volcano monitoring. Salinity monitoring. Seabed and deep sea
exploration. Oil and Gas Industry
Seabed survey. Pipeline monitoring. Intervention. Iceberg reconnaissance.
Marine Science Research Marine Biology Research Oceanography Studies
Shipwreck Reconnaissance Fisheries
• Defense – Reconnaissance. – Monitoring. – Detection. – Surveillance.
• Customs • Underwater power lines
– Line laying. – Line monitoring
Applications of AUVs
Data logging Data logging
Navigation Rate gyro,
Where am I going? How do I get there?
Control: To ensure that the various performance parameters are achieved in the system: eg: Speed, Acceleration, etc.
Guidance: To ensure that the commanded path is followed by the system
Navigation: To inform the MCS about the present status of the vehicle wrt the commanded position as well as react to the environmental changes
18
Mathematical Model of the AUV
where M = MRB+MA = Inertia matrix MRB = Rigid body Inertia Matrix MA = Added mass matrix D() = Damping matrix = Control inputs = Bf = FT + FCP
FT = Propulsion forces and moments FCP = Control plane forces and moments C() = C()RB+ C()A = Coriolis and Centripetal Matrix g() = Restoring matrix (gravitational and buoyancy effects)
= Linear and angular velocities w.r.t. body (moving) frame = [u v w p q r]T
= Linear and angular displacements w.r.t. inertial (fixed) frame= [x y z ]T
The AUV kinematics can be expressed as
τ)g(ην)D(νν)C(ννM
f)η(B)η(gη)ηη,(Dη)ηη,(Cη)η(M η
The AUV dynamic model with respect to the earth fixed frame of reference becomes
)))))((1 g(ηνD(ννC(ντMν
z i n m
Only one propulsion thruster activated, CB=[0 0 -7]mm,& CG=[0 0 0]mm and the vehicle net buoyancy effect is equal to+25kgf
0 100 200 300 400 500 600 700 800 900 1000 -100
-80
-60
-40
-20
0
20
r D
Hybrid control structure
Tracking control structure
Trajectory tracking requires the design of control laws that guide the vehicle to
track an inertial trajectory, i.e., a 3D path on which a time law is specified.
Guidance System
path planning
from the navigation and generate references for the vehicle
control system so as the vehicle can move through a set of
way points as per the given sequence.
Time variant trajectory tracking or time invariant path
following.
avoidance, minimum time navigation, fuel optimization and
weather routing.
Both next and the previous waypoints are
considered.
waypoints.
A distance threshold is fixed at both sides of the
waypoint
Heading correction is calculated when AUV is
moving towards a waypoint as well as starts
leaving the same waypoint.
Desired heading= reference heading +
Goal Point: (100,0)
during course change at waypoints.
Methodology for algorithm development
dψ
rψ
cψ
mLwhereLThreshold
Lo
2
vAi
2
vAi
2


Normalized difference between the auxiliary point Ai and waypoint from the vehicle position:
Distance between vehicle position and current way-point
: 2
vi
2
vi
2
)(f).dP(f).(
)(f).dP(f).(
AiaCiACscc
AiaCiACscc
Map the spherical coordinate to 3D Cartesian coordinate
)sgn(
)sgn(
1ririsci
1ririsci
DETERMINE: The reference heading
and path angle in spherical coordinates DEFINE: Distance threshold points at
both sides of the current waypoint
FIND: The sign of correction heading
and path angle to be required for
smooth turn
smooth turn
coordinates to Cartesian coordinates
by linear curve fitting
BRING: Vehicle position to coincide with
the tangential line to current waypoint
Is current vehicle
previous, current and next waypoints in
3D Cartesian coordinates
SP: (20,50,0), WP: (0,0,0),(75,75,75), (150,150,150), (250,250,250) & (300,300,300), GP:
(500,500,500)
(150,120,0)
0
50
100
150
-5
0
0
5
10
15
20
25
30
35
40
45
50
Bakaric method
Improved LOS
Multipoint potential field method for Obstacle
avoidance in 3D space
points:
point pj:
3/17/2016 36
Total repulsive potential Urep at qi due to
the obstacle:
the vehicle:
equation as:
Minimum potential:
potential position
horizontal plane is defined by
where,
fauvobsCZ dRRR
Rcz- range of critical zone, Robs- radius of obstacle, Rauv- radius of AUV, df- distance factor
Divide each zone into 3 sectors
Check for the presence of obstacle in the
sectors
Activate strategy to avoid
points
checking local minima
CALCULATE: Attraction potential
FIND: The complete Repulsion
CALCULATE: Total potential
DETERMINE: Minimum total
activate hovering thruster
Buoyancy: Positive
3/17/2016 39
Both static and dynamic obstacles are assumed and they are in spherical shape of various
sizes. The vehicle is reduced in size to a single point, and the obstacles are enlarged by the
vehicle’s radius.
Two forward looking sonar sensors are considered to sense the obstacle. One sensor with a
range of 40m, horizontal & vertical beam widths of 1200 and 300 is fixed horizontally.
The other sensor with a range of 40m, horizontal & vertical beam widths of 300 and 1200
is fixed vertically.
The velocity of the obstacle is not more than the velocity of the vehicle.
The vehicle is underactuated and cannot move sideways and roll of the vehicle is
neglected.
Depth rating:100m
The positive scaling factors and are taken as 1 and 0.1. The distance influence threshold is
The forward speed (ud) of the vehicle is fixed to a constant value. This value is used to fix
the radial distance (dq ) which is used for defining the next one-step positions.
A sphere of acceptance with radial distance of 2m is considered at goal position.
Assumptions
0
50
100
50
100
50
100
Simulation results
SPPF MPPF
17.1675 21.935 5
18.1688 22.512 4
20
40
60
80
100
SPPF method
MPPF method
0 10 20 30 40 50 60 70 80 90 100 40
50
60
70
80
SPPF
0 10 20 30 40 50 60 70 80 90 100 40
50
60
70
80
0
5
10
15
0
5
10
15
5
10
15
20
25
30
5
10
15
20
25
30
5
10
15
20
25
30
0
2
4
0
2
4
20
40
60
80
100
42
5
10
15
20
25
30
5
10
15
20
25
30
0
2
4
0
2
4
5
10
15
20
25
30
5
10
15
20
25
30
-10
-5
0
5
10
15
-10
-5
0
5
10
15
GP
SP
5
10
15
20
25
30
NPF No 37.9873m
MPPF No 33.3371m
Method Chance of
Desired path
Actual path
obstacle path
In MPPF method the distances to the obstacles are increased by 27.8% compared to the conventional method, thus providing a safe path for AUV. Similarly it was observed that the path length is reduced by 17.9%
User Input
The AUV dynamics can be expressed as:
where, M=Mass matrix, C()= Coriolis and Centripetal Matrix, D()= Damping matrix g() = Gravitational and buoyancy effects, = Control inputs
The above equation can be written as, )))g(ην)D(νν)C(ν(τ(Mν 1
)(g)(D)(CM Body
Simulation results
50
100
150
100
200
50
100
0
50
Obstacle
avoidance
GUI
(ControlDesk)
3/17/2016 46
Hardware-In-Loop (HIL) simulation is a technique that is used in the
development and test of complex real-time systems.
It replaces the emulated hardware under test or control logic in the
simulation model, with real hardware that interacts with the computer
models.
It provides the realism of the simulation and provides access to
hardware features currently not available in software-only simulation
models. HIL architecture
0
20
0
20
0
20
0
5
0
5
0
5
0
20
0
20
Error MIL HIL
xe (m) 0.6018 0.9573
ye (m) 0.3237 0.3528
ze (m) 0.3678 0.2187
Pitche (deg) 2.2558 2.4734
Yawe (deg) 1.4646 1.8525
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10 4
Surge error
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10 4
Sway error
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10 4
-50
0
50
-50
0
50
gas Industry, Oceanographic explorations and defence
areas.
present challenges in AUV development
References 1. Fossen, T., 1994. Guidance and Control of Underwater Vehicles. Wiley,
New York. 2. www.rov.org 3. D. Richard Blidberg, “The Development of Autonomous Underwater
Vehicles (AUV); A Brief Summary”, Autonomous Undersea Systems Institute technical report, Lee New Hampshire, USA.