-
Path Planning and Obstacle Avoidance Strategies for
Autonomous Underwater Vehicles
Prof. T. AsokanIndian Institute of Technology Madras
-
Areas of Research and development
1. Autonomous Underwater Robots: Design, Modelling, and Control
2. Dynamic station keeping control of ROV3. Multipoint Potential Field method for
obstacle avoidance
4. AUV-Manipulator Systems
5. Surgical Robotics- Design and Development of Tele-surgical trainer robot (Filed 2 patents)
6. Tremor compensation in surgical robotics7. 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)
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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
NSTL Vizag
-
Contents
Introduction
Underwater Robots
Atonomous Underwater Vehicles
AUV Design
Design of Controllers
Path Planning
Obstacle Avoidance
Experimental Methods
Conclusions
-
UNDERWATER WORKING METHODS
-
3/17/2016 8
Why Underwater Robots ?
Motivation
US$300 B offshore industry Demand for pipeline survey and
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:
Institutes: University of Tokyo, MIT, Hawai University, WoodsholeOceanographic Institute, etc
Industry:
Alstom Schilling, Perry Tritech, TritechInternational etc..
-
Human Occupied Vehicle (HOV)
ALVINWoods hole Oceanographic Institute
-
3/17/2016 10
Unmanned Robotic Vehicles
OperationMode
Task Complexit
y
Tele-operation Supervisory Autonomous
ROV
HybridURV
AUV
Hybrid URV canswitch to direct tele-operation mode forcomplex 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
Mother ship to follow the ROV
Pilot needs to be trained well
Operator fatigue affects mission results
Tether management is the main challenge
Operationally efficient compared to other class of vehicles
-
ROV Deployment and Applications
Diver Observation Platform Inspection Pipeline Inspection Surveys Drilling Support Construction Support Debris Removal Platform Cleaning
Sub-sea Installations
Telecommunications Support Object Location and Recovery
-
Autonomous Underwater Vehicle (AUV)
It is a robotic device that is driven through the water by apropulsion system, controlled and piloted by an onboardcomputer 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 systemson 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)
-
Geological Survey Earthquake detection and
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
Academic Research. Development. Teaching. Practical applications. Projects.
Defense Reconnaissance. Monitoring. Detection. Surveillance.
Customs Underwater power lines
Line laying. Line monitoring
Delivery Systems Telecommunications Repair and Rescue
Applications of AUVs
-
Main Building blocks of AUV Control System
Data loggingData logging
management
CommunicationsRF and Acoustic modem
NavigationRate gyro,
Compass,
Depth or Pressure sensor,
Doppler log,
Echo sounder,
GPS
Mission ControlOcean Sensors
Vehicle SafetyActuator Control
Thrusters, rudder and
stern planes
Vehicle
Guidance and
Control
Set
points
Reference
Trajectory
Vehicle
Dynamics
NavigationSystem
support data
-
Control, Guidance, and Navigation
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 matrixMRB = Rigid body Inertia MatrixMA = Added mass matrixD() = Damping matrix = Control inputs = Bf = FT + FCPFT = Propulsion forces and momentsFCP = Control plane forces and momentsC() = C()RB+ C()A = Coriolis and Centripetal Matrixg() = 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
)g()D()C(M
)(J where, J() is the kinematic transformation (Jacobian) matrix.
f)(B)(g),(D),(C)(M
The AUV dynamic model with respect to the earth fixed frame of reference becomes
-
)))))((1 g(D(C(M
-
0
5
10
15
20
25
30
-25
-20
-15
-10
-5
0
5
-100
0
100
200
300
400
500
600
700
800
x in m
3D plot
y in m
z in 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
Time (t) in Sec
Angula
r D
ispla
cem
ent
in d
eg
Angular Displacement vs Time
Roll
Pitch
Yaw
-
21
Closed-loop (feedback) Control
Feedback
Control system
Environmental
Disturbances
AUV Dynamics AUV Kinematics
Thruster
Forces and
moments
JM-1
g(.)
C+D
Thruster
dynamics
d
d
Rudder &
stern plane
Forces and
moments
Control planes
dynamics
GNC
Sensor
System
Mission
control
d
Sensor
Noises
+
+
+
+
+_+
_
+
e
-
Vehicle Control System
-
23
Tracking Controller Design
Model reference control structure
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
Development of strategy and algorithm for obstacle avoidance and
path planning
Guidance Systems: Receive target related informationfrom 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.
Guidance systems include waypoint guidance, obstacle
avoidance, minimum time navigation, fuel optimization and
weather routing.
-
Way-point guidance by Improved LOS method
Both next and the previous waypoints are
considered.
Auxiliary points are fixed on both sides of the
waypoints.
A distance threshold is fixed at both sides of the
waypoint
Vehicle calculates the heading correction only
between the threshold points and waypoints.
Heading correction is calculated when AUV is
moving towards a waypoint as well as starts
leaving the same waypoint.
Desired heading= reference heading +
correction heading.
3/17/2016 26
2D path following
WP1
WP3
WP2o
VP
r
Circle of acceptance
d
Methodology for 2D LOS
method
-
Starting Point: (50,50)
Way Point: (0,200),(300,350),(450,50),(300,200) and (100,150)
Goal Point: (100,0)
3/17/2016 27
-
Animation 1
Animation 2
../Videos/los4.avi../Videos/LOS5.AVI.avi
-
Figure : Calculation of heading correction for smooth turn
during course change at waypoints.
Methodology for algorithm development
crd
- desired heading,- actual reference heading- heading correction
d
r
c
mLwhereLThreshold
Lo
5.4,*10
,*2
),( iii yxWP
),( AiAii yxA
idA
idP
Aio),( BiBii yxB
idB
GP
),( )1()1(1 iii yxC
),( iii yxD
),( iii yxC
),( )1()1(1 iii yxD
),( 111 iii yxWP
SP
cr
r
rcVP
idP CidP
DidP
3/17/2016 29
-
30
Distance between vehicle position and auxiliary point Ai:
2
vAi
2
vAi
2
vAiAi )zz()yy()xx(d
o
CiAiAi
dPd
Normalized difference between the auxiliarypoint Ai and waypoint from the vehicle position:
Distance between vehicle position and current way-point
: 2vi
2
vi
2
viP )zz()yy()xx(d ci
Sign of orientation correction between Ci and WPi :
)(f).dP(f).(
)(f).dP(f).(
AiaCiACscc
AiaCiACscc
[Bakaric et al. (2004)]
Orientation correction between Ci and WPi:
Desired orientation can be calculated as
Map the spherical coordinate to 3D Cartesian coordinate
)sgn(
)sgn(
1ririsci
1ririsci
,ciridi
ciridi
-
No
Ye
s
No
Ye
s
Ye
s
Ye
sNo
No
DETERMINE: The reference heading
and path angle in spherical coordinatesDEFINE: 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
COMPUTE: The correction heading
and path angle to be required for
smooth turn
CALCULATE: The desired heading
and path angle and map the spherical
coordinates to Cartesian coordinates
Is current vehicle
position =
waypoint1?
Start
Is number of
waypoints is
equal to zero?
DEFINE: Auxiliary points at both
sides of the current waypoint
DEFINE: Distance and Angle factors
by linear curve fitting
Is current vehicle
position = goal
position?
DEFINE: Aiming point distance,
reference angles and instantaneous angles
BRING: Vehicle position to coincide with
the tangential line to current waypoint
Is current vehicle
position= first
distance threshold
point?
Stop
OBTAIN: Vehicle and Goal position,
previous, current and next waypoints in
3D Cartesian coordinates
SELECT: The next waypoint
-
3/17/2016 32
0
50
100
150
0
20
40
60
80
0
2
4
Surge(x),[m]Sway(y),[m]
Heav
e(z
),[m
]VP
GP
WP
Basic LOS
C1
D1
A1
B1
Improved LOS
0
100
200
300
400
500
0
100
200
300
400
500
0
100
200
300
400
500
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
Basic LOS
Improved LOS
0
100
200
300
0
50
100
150
0
10
20
30
40
50
60
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
Basic LOS
Improved LOS
SP: (10,0,2), WP: (0,0,0) & (75,0,75) GP: (150,0,0)
SP: (10,0,2), WP: (0,0,0) & (75,75,0) GP: (150,0,0)
SP: (20,50,0), WP: (0,0,0),(75,75,75), (150,150,150), (250,250,250) & (300,300,300), GP:
(500,500,500)
Simulation results
SP: (150-20,0), WP: (150,-20,0),(0,0,50), (350,0,50), (200,50,50) (350,100,50) & (10,100,50), GP:
(150,120,0)
0
50
100
150
-5
0
50
20
40
60
80
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
Basic LOS
Improved LOS
-
0 20 40 60 80 100
0
5
10
15
20
25
30
35
40
45
50
Surge(x),[m]
Sw
ay(y
),[m
]
Basic LOS method
Smoothing spline for basic LOS
Bakaric method
Smoothing spline for bakaric method
Improved LOS
Smoothing spline for Improved d LOS method
-
Multipoint Potential Field Method for Obstacle Avoidance
-
Multipoint potential field method for Obstacle
avoidance in 3D space
factorscalingpositiveais
intpogoalandvehiclethearound
intpoqbetweencetandistheisqqdth
iiaa
2
aatt iid
2
1)q(U
planeverticalinangleturningMaximum
planehorizontalinangleturningMaximum
m
m
aq(b)
v
v
r
z y
x
Goal
1q
42q
vA
41q
49q
mhv
mhv
41ad
z
y
x
Goal
h
h
Z
YX Reference
Frame
Body Fixed Frame
(a)
[Ge and Cui
(2000)]
Attractive potential Uatt at qi points:
3/17/2016 SSK,IITM 35
-
r
r
R
zy
Goal
vA
41q
90
90
hhm
hhm
x
3
7
1
4
2
6
5
1
5
34153od
4113od
to
to
2
a
2
to
obs
ddif,0
ddif,dd
1
d
1
2
1
)q(U
j,i
j,ii
j,i
j,i
factorscalingpositiveais
thresholdluenceinfcetandistheisd,obstacletheon
intpopandintpoqbetweencetandistheis|pq|d
t
th
j
th
ijio
sensorofwidthbeamvertical
sensorofwidthbeamhorizontal
bm
bm
[Ge et al.(2000)]
Repulsive potential Uobs at qi due to the obstacle
point pj:
3/17/2016 36
sA
Calculating radius of
obstacle
Read Input: Sensor data
Generate multiple points on the obstacle
Calculate repulsive potential for the obstacles
-
Total repulsive potential Urep at qi due to
the obstacle:
Total potential Utot at each point around
the vehicle:
The above steps can be written in a single
equation as:
Minimum potential:
Command the vehicle to the minimum
potential position
Local minima problem
3/17/2016 37
)q(U)q(U
K
1j
obsrep j,ii
R
1m
repatttot )q(U)q(U)q(U m,iii
to
to
2
a
2
toK
1j
R
1m
atttot
ddif,0
ddif,dd
1
d
1
2
1
qU)q(U
m,j,i
m,j,ii
m,j,i
ii
)Umin(U tottotmin
rvCZ and,R Critical (local minima) zone in
horizontal plane is defined by
where,
,and,R vrCZ
Critical zone in vertical plane is defined by:
fauvobsCZ dRRR
Rcz- range of critical zone, Robs- radiusof obstacle, Rauv- radius of AUV, df-distance factor
Divide each zone into 3 sectors
Check for the presence of obstacle in the
sectors
Declare local minima, if exists
Activate strategy to avoid
-
Yes
No
Yes
No
Stop
Start
GIVEN: Starting and Goal
positions
DISCRETIZE: The region on a
hemisphere of radius r meter
around the vehicle into equiangular
points
DEFINE: Critical Zone (CZ) for
checking local minima
CALCULATE: Attraction potential
(Uatt)
CALCULATE: Repulsion potential
by discretizing obstacle into K2
points (Uobs) for each obstacle
FIND: The complete Repulsion
potential (Urep) for the obstacles
CALCULATE: Total potential
(Utot) for each point around AUV
DETERMINE: Minimum total
potential (Umintot)
COMMAND: Move the vehicle
towards the minimum potential
point (Umintot)
GET: Obstacles data from sensor
DECLARE: local minimum exists and
activate hovering thruster
Does Local
minima Exist?
Is current
position= goal
position
-
Simulation studies
Configuration: Flat-fish type
Design speed: 4 knots (max)
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
vehicles 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.
Length (over all): 4.6m
Width (over all): 1.6m
Height: 0.7m
Propulsion thrusters: 2
Maneuvering thrusters: 3
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
)RR(2d auvobst
-
SP =(0,0,0)
GP=(90,90,90)
0
50
100
0
50
1000
20
40
60
80
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m] 0 20 40 60 80 100
0
50
100
Sw
ay(y
),[m
]
Surge(x),[m]
0 20 40 60 80 1000
50
100
Hea
ve(
z),[
m]
Sway(y),[m]
0 20 40 60 80 1000
50
100
Surge(x),[m]
Hea
ve(
z),[
m]
3/17/2016 40
0
50
100
0
50
1000
20
40
60
80
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
Vel=2m/s
Vel=1.5m/s
Vel=1m/s
050
1000
50
100
0
50
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
SP=(10,15,5)
GP=(83,84,78)
Local minimum near SP
0
50
100
0
50
100
0
50
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
0
50
100
0
50
100
0
50
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
Local minimum due to concave shape obstacle
Simulation results
-
SPPF MPPF
Distance between obstacle 1 and vehicle (m)
17.1675 21.9355
Distance between obstacle 2 and vehicle (m)
18.1688 22.5124
Significance of MPPF method
3/17/2016 41
0 20 40 60 80 1000
20
40
60
80
100
Surge(x),[m]
Sw
ay(y
),[m
]
SPPF method
MPPF method
0 10 20 30 40 50 60 70 80 90 10040
50
60
70
80
Surge(x),[m]
Sw
ay(y
),[m
]
SPPF
0 10 20 30 40 50 60 70 80 90 10040
50
60
70
80
Surge(x),[m]
Sw
ay(y
),[m
]
MPPF
0 20 40 60 80 100 -1000100
0
5
10
15
Sway(y),[m]Surge(x),[m]
SPPF
Rep
uls
ion
po
ten
tial
0 20 40 60 80 100 -1000100
0
5
10
15
Sway(y),[m]Surge(x),[m]
MPPF
Rep
uls
ion
po
ten
tial
SPPF MPPF
Path
Length
79 67
0 5 10 15 20 25 300
5
10
15
20
25
30
Surge(x),[m]
Sw
ay(y
),[m
]
D.Fu-guang method
Proposed method
Surge(x),[m]
Sw
ay(y
),[m
]
SPPF
5 10 15 20 25 30
5
10
15
20
25
30
Surge(x),[m]
Sw
ay(y
),[m
]
MPPF
5 10 15 20 25 30
5
10
15
20
25
30
0 10 20 30 0102030
0
2
4
Sway(y),[m]
SPPF
Surge(x),[m]
Rep
uls
ion
po
ten
tial
0 10 20 30 051015202530
0
2
4
Sway(y),[m]
MPPF
Surge(x),[m]
Rep
uls
ion
po
ten
tial
0
50
100
0
50
1000
50
100
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
0 20 40 60 80 1000
20
40
60
80
100
Surge(x),[m]
Sw
ay(y
),[m
]
SPPF method
MPPF method
-
Comparison of MPPF method with other methods
42
Surge(x),[m]
Sw
ay(y
),[m
]
SPPF
5 10 15 20 25 30
5
10
15
20
25
30
Surge(x),[m]
Sw
ay(y
),[m
]
MPPF
5 10 15 20 25 30
5
10
15
20
25
30
0 10 20 30 0102030
0
2
4
Sway(y),[m]
SPPF
Surge(x),[m]
Rep
uls
ion
po
ten
tial
0 10 20 30 051015202530
0
2
4
Sway(y),[m]
MPPF
Surge(x),[m]R
epu
lsio
n p
ote
nti
al
0 10 20 300
5
10
15
20
25
30
Surge(x),[m]
Sw
ay
(y),
[m]
0 5 10 15 20 25 300
5
10
15
20
25
30
Surge(x),[m]
Sw
ay(y
),[m
]
VFF method
MPPF method
-15 -10 -5 0 5 10 15-15
-10
-5
0
5
10
15
Surge(x),[m]
Sw
ay
(y),
[m]
SP
GP
MPPF
APF
NPF
-15 -10 -5 0 5 10 15-15
-10
-5
0
5
10
15
GP
SP
Surge(x),[m]
Sw
ay(y
),[m
]
CS boundary
MPPF method
NPF method
APF method
0 5 10 15 20 25 300
5
10
15
20
25
30
Surge(x),[m]
Sw
ay
(y),
[m]
VFF method
MPPF method
Method Local
minimum
Distance travelled to
reach goal position
APF Yes Couldnt reach
NPF No 37.9873m
MPPF No 33.3371m
Method Chance of
collision
Distance travelled to
reach goal position
APF Yes 22.36m
NPF No 37.9873m
MPPF No 33.3371m
-
020
4060
80100
120
-50
0
50
-50
0
50
Surge(x),[m]Sway(y),[m]
Hea
ve(
z),[
m]
SP
GP
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
Obstacle avoidance and
way-point guidance
Control Plane
Trajectoryplanner
Sensor data
Controller
ThrusterPath
elements
Desired
states
Tracking
error
Speed
Angle
Actuators
Force/TorqueActual states
+-
The AUV dynamics can be expressed as:
where,M=Mass matrix, C()= Coriolis and Centripetal Matrix, D()= Damping matrixg() = Gravitational and buoyancy effects, = Control inputs
The above equation can be written as, )))g()D()C(((M1
)(g)(D)(CMBody
frame
Inertial
frame
x,
z,
y,
p,pK,
u,uX,
q,qM,
v,v,Y
w,w,Z r,r,N
AUV Frame assignment
3/17/2016 44
Robot Dynamic
Model
Integrating high level control with AUV dynamics
-
Simulation results
3/17/2016 450
50
100
150
200
2500
100
200
-100
0
100
Surge(x),[m]Sway(y),[m]
SP
WP
GP
Desired
Actual
0 50 100 1500
50
100
150
Surge(x),[m]S
way
(x),
[m]
SP
GP
WP
Desired path
Actual path
0 20 40 60 80 100 1200
100
200
Time(t),[s]
Su
rge(
x),
[m]
0 20 40 60 80 100 1200
50
100
Time(t),[s]
Sw
ay(y
),[m
]
0 20 40 60 80 100 120-50
0
50
Time(t),[s]
Yaw
(),
[deg
]
Desired
Actual
0 50 100 150
-50
0
50
-50
0
50
Sway(y),[m]
Surge,(x),[m]
Hea
ve(
z),[
m]
SP
GP
Desired Path
Actual Path
0
50
100
150
-100
-50
0
50
100-50
0
50
Surge(x),[m]Sway(x),[m]
Hea
ve(
z),[
m]
SP
GP
WP
Desired path
Actual path
Avoiding local minima Combined result
Obstacle
avoidance
3D way-point guidance
2D way-point guidance
-
Hardware-In-Loop (HIL) Experiments
dSPACE DS1104 R&D Controller
GUI
(ControlDesk)
Actuator Unit
(Thrusters and
Control planes)
Interface
Unit
(Connector
panel)
Sens
or
User
Input
Sensor
data
Measured pitch angle
Measured yaw angle
Measured
speed
Commanded pitch
angle
Commanded yaw
angle
Commanded
speed
Obstacle Avoidan
ce
Trajectory
Planner
AUV Controll
er
AUV Dynami
cs
Desired states
Path element
s
Commanded speed & angle
ForceSP & GP
dSPACE DS1104 R&D
controller
Actualstates Actuato
r Dynami
cs
Measured speed & angle
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
-
3/17/2016 47
Thruster
Rudder motorConnector panel
Driver boards
Stern motor
RTI model
HIL setup
-
ControlDesk Layout
3/17/2016SSK,IITM
48
HIL test using obstacle sensor
3DHILcase-1.avi3DHILcase-1.avieditedwosen.avieditedwosen.avi
-
3/17/2016SSK,IITM
49
HIL simulation results
0 20 40 60 80 100 120-20
0
20
Surge(x),[m]
Sw
ay(y
),[m
]
-10 -5 0 5 10 15 20-20
0
20
Sway(y),[m]
Hea
ve(
z),[
m]
Desired
Actual
0 20 40 60 80 100 120-20
0
20
Surge(x),[m]
Hea
ve(
z),[
m]
0 50 100 150-5
0
5
Time(t),[s]
xe,[
m]
0 50 100 150-5
0
5
Time(t),[s]
ye,[
m]
0 50 100 150-5
0
5
Time(t),[s]
z e,[
m]
0 50 100 150-20
0
20
Time(t),[s]
e,[
deg
]
0 50 100 150-20
0
20
Time(t),[s]
e,[
deg
]
Comparison between MIL and HIL
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 104
-1
0
1x 10
4
Xcorr
ela
tion
Lags
Surge error
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
-1000
0
1000
Xcorr
ela
tion
Lags
Sway error
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
-1000
0
1000
Xcorr
ela
tion
Lags
Heave error
Error No. of samples
delayed
xe (m) 34
ye (m) 84
ze (m) 89
020 40 60 80 100
-50
0
50
-50
0
50
Sway(y),[m]Surge(x),[m]
Hea
ve(
z),[
m]
SP
GP
Desired Path
Actual Path
-
Video
../Videos/Case1A_HILdyn3Donline.avi
-
Summary
Underwater Robots are finding wide applications in Oil and
gas Industry, Oceanographic explorations and defence
areas.
AUVs are the promising robots for the future
Guidance, Navigation, Control, and power source are the
present challenges in AUV development
References1. Fossen, T., 1994. Guidance and Control of Underwater Vehicles. Wiley,
New York.2. www.rov.org3. D. Richard Blidberg, The Development of Autonomous Underwater
Vehicles (AUV); A Brief Summary, Autonomous Undersea SystemsInstitute technical report, Lee New Hampshire, USA.
http://www.rov.org/
-
Thank you