Index
Note: Page numbers followed by f indicate figures, t indicate tables and b indicate boxes.
A
Absolute sensors 207
Adaptive and Generic Accelerated Segment Test (AGAST) 277
Aerial mobile systems 4
control 392
decision making 392
localization and mapping 390–391
path planning 392
sensors 389–390
in agriculture 393–398
in domestic environments 403–410
in industry 399–403
in walking rehabilitation therapy 410–416
aerial mobile systems 4
applications 6–7
commands 5
future aspects 7
ground mobile systems 4
history of 7–9
mechanical and electronic parts 6
properties 6
water and underwater mobile systems 4
B
environment sensing 320–325
in environment 332–337
principle 318–320
motion, in environment 326–332
Bayesian theorem 312
Behavior-based agent operation 458–459
Brockett’s condition 91–92
C
Cascade control schemas 61–62
Cathetus 75
Chow’s theorem 44
Closed list 192–193
Closed-loop transfer function 63–65
Collision detection methods 180–181
Color sensor calibration 443–444
Configuration space 163–164
Control error 63
Controllability 44–47
Controller gains 102–103
orientation and rotation 208–217
projective geometry 221–230
rotating frames 219–221
translation and rotation 217–218
Cost-to-goal 199
D
DCM See Direction cosine matrix (DCM)
inertial navigation system 232–239
odometry 231–232
feedback action 91–94
feedforward 91–94
reachable velocities and motion constraints 36b
PDC control of 125
Takagi-Sugeno fuzzy error model of 122–124
definition 35
involutive 36
Dynamic constraints 32–33
Dynamic environment 161–162
E
Environment sensing 320–325
Epipolar constraint 224–225
Essential matrix 224–225
Estimate convergence and bias 300–301
Euler angles 209–210
Euler integration method 421–423
Extended Kalman filter (EKF) 351–374
External kinematic model 232
Exteroceptive sensors 286
F
Feedback action 91–94
Feedback control part 61
flat outputs 94
reference trajectory 95–96
state-space representation 95–96
translational velocity 95–96
Feedforward 91–94
Flat outputs 92–93
reference point 67
translational velocity 66–67
Four-state error model 117–122
Frequency spectrum 300
Frobenious theorem 39
Fundamental matrix 224–225
Future reference error 127–128
G
Gaussian function 293–294
Gaussian noise 351
Ground mobile systems 4
H
Heading measurement systems 239–240
Histogram filter 375
Holonomic constraints 32–34
Hue-saturation-lightness (HSL) 270–271
I
Image-based visual servoing (IBVS) 465–466
Image features, camera 268–282
Inertial navigation system (INS) 232–239
motion sensors 232–233
pose error 233
rotation sensors 232–233
self-contained technique 232–233
signal-to-noise ratio 233
unit angular rate 233–234
Information filter 375
Informed algorithms 193
Infrared light sensor 420–421
INS See Inertial navigation system (INS)
Instantaneous center of rotation (ICR) 14
Internal camera model 222–223
J
Joint probability 290
K
Kalman-Bucy filter 375
continuous variable, probability distribution of 338f
correction covariance matrix 348
derivatives 374–375
extended 351–374
Gaussian function 338
Gaussian noise 346
for linear system 346–347
in matrix form 345–351
prediction step of 342–344
state estimation 337
updated state variance 341–342
Kalman observability matrix 303
Kinematic constraints 32–33
Kinematic model 13
external kinematics 14
instantaneous center of rotation (ICR) 14
internal kinematics 13–14
motion constraints 14
omnidirectional drive 27–31
tracked drive 31–32
L
Laser range finder (LRF) 254–255
Least squares method 254
Lie brackets 36
Lie derivative operator 302–303
laser range finder (LRF) 447–448
localization, using odometry 448–449
virtual train formation 447
Linear matrix inequality (LMI) 122
Line-extraction algorithms 254
Localization 332
algorithm 318–319
in environment 332–337
correction step 438–439
global navigation satellite system (GNSS) 433
prediction step 435–438
colored tiles 440–447
manual control 442
wheel odometry 442–443
Local weak observability 302–303
Locomotion 13
LRF See Laser range finder (LRF)
Lyapunov-based control design 105–122
four-state error model 117–122
periodic control law design 112–117
control design in 106–112
Lyapunov stability 106
control design in 106–112
M
Machine vision algorithms 279–280
Manhattan distance 199–200
Manual control 442
Maps 282–283
Maximally Stable Extremal Regions (MSER) 277–278
Memory usage 168–169
Mobile 2–3
image-based visual servoing (IBVS) 465–466
natural image features 466
position-based visual servoing (PBVS) 463–465
controllability 44–47
dynamic constraints 32–33
holonomic constraints 32–34
integrability 35
kinematic constraints 32–33
lie bracket 35–44
nonholonomic constraints 32–35
vector fields and distribution 35–44
in environment 326–332
sensors 232–233
controllability 44–47
dynamic constraints 32–33
holonomic constraints 32–34
integrability 35
kinematic constraints 32–33
Lie bracket 35–44
nonholonomic constraints 32–35
vector fields and distribution 35–44
dynamic models 13
differential drive vehicle 51–58
Lagrange formulation 48
state-space representation 49–50
external kinematics 14
instantaneous center of rotation (ICR) 14
internal kinematics 13–14
motion constraints 14
omnidirectional drive 27–31
tracked drive 31–32
behavior-based agent operation 458–459
obstacle avoidance 458
Multiview geometry 224–228
N
Natural local image features 269–270
Noise linearization 352
Noise modeling 439
environment sensing 320–325
localization, in environment 332–337
localization principle 318–320
motion, in environment 326–332
state estimation 304–318
derivatives 374–375
extended 351–374
in matrix form 345–351
Bayes’ rule 295–299
disturbances and noise 299–300
estimate convergence and bias 300–301
observability 301–303
Noninformed algorithms 193
Nonlinear tracking error model 123–124
O
Objective function 132–133
Observability 301–303
rank condition 302–303
Obstacle avoidance 458
Occupancy grid 168
drifting 443
four-wheel Mecanum drive 28–30
Open list 192
friction force 148–149
radial acceleration 148–150
reference path 148
reference trajectory 151
tangential acceleration 148–150
velocity profile 148
Euler angles 209–210
quaternions 210–217
Orientation control 63–66
for Ackermann drive 65–66
for differential drive 63–65
Orientation error 65–66
P
Parallel distributed compensation (PDC) 122
Parallel projection 221
Parameterization 210
Bayesian filter 375
Gaussian probability distribution 377
in prediction part 377
probability distribution 376
from known robot motion 468
model predictive control (MPC) 138–141
Periodic control law design 112–117
Pole placement approach 102–103
Pose error 233
active markers measurement 240–252
dead reckoning 231–239
environmental features, navigation using 253–282
global position measurement 240–252
heading measurement systems 239–240
maps 282–283
Position-based visual servoing (PBVS) 463–465
Bayes’ rule 295–299
Projective geometry 221–230
3D reconstruction 228–230
internal camera model 222–223
multiview geometry 224–228
parallel projection 221
perspective projection 221
singular cases 228
Proportional controller 85
Proprioceptive sensors 286
Q
R
Random Sample Consensus (RANSAC) method 280–282
Reference orientation 84–85
forward-motion control 66–70
for Ackermann drive 65–66
for differential drive 63–65
reference position 70–71
Reference trajectory 126
Relative sensors 207
Robot(s) 1–2
free space 161–162
Robot kidnapping 447
Robot-tracking prediction-error vector 128
Rossum’s Universal Robots (R.U.R.) 1
rotZ function 237
orientation and rotation 208–217
translation and rotation 217–218
Rotation sensors 232–233
S
Scale invariant feature transform (SIFT) 277
Self-contained technique 232–233
Sensors 289
absolute sensors 207
characteristics 284–286
classifications 286–287
orientation and rotation 208–217
projection 221–230
rotating frames 219–221
translation and rotation 217–218
active markers and global position measurement 240–252
dead reckoning 231–239
features 253–282
heading measurement systems 239–240
maps 282–283
relative sensors 207
Signal distribution 300
Signal-to-noise ratio (SNR) 233
Simultaneous localization and mapping (SLAM) 253
Smooth time-invariant feedback 91–92
Speeded-Up Robust Features (SURF) 277
Speedup algorithms 271–273
from observations and actions 311–318
disturbances and noise 299–300
estimate convergence and bias 300–301
observability 301–303
State transition graph 166
Static environment 161–162
Stochastic optimization 132–133
Straight-line features 254–268
T
Takagi-Sugeno fuzzy control design 122–125
3D reconstruction, stereo camera configuration 228–230
Total probability theorem 313
feedback action 91–94
feedforward 91–94
kinematic trajectory-tracking error model 100–101
linear controller 101–105
Lyapunov-based control design 105–122
model-based predictive control (MPC) 125–132
particle swarm optimization-based control (PSO) 132–141
Takagi-Sugeno fuzzy control design 122–125
visual servoing (VS) approaches 142–147
Transfer function 63–65
Translational velocity, Cartesian components of 92–93
Translation and rotation 217–218
Trapezoidal numerical integration 232
2D Gaussian function 293–294
Two-line element set (TLE) 251
U
Unscented Kalman filter 374
V
Vector fields 35
Vehicle kinematic model 46–47
Velocity command 71
camera retreat 142
control error 142
features 142
hybrid VS 142–143
image-based visual servoing (IBVS) 142–143
interaction matrix 143–144
Lyapunov function 144–145
position-based visual servoing (PBVS) 142–143
velocity controller 143–144
in Matlab 174–176b
W
Water and underwater mobile systems 4
control strategies 397–398
localization, mapping, and slam 397
planning routes and scheduling 398
service unit setup 394–397
control 408–409
decision making 409–410
localization and mapping 407
path planning 408
sensors 405–407
control 402–403
decision making 403
localization and mapping 401–402
path planning 403
sensors 401
optimal velocity profile estimation 148–157
to reference pose 62–87
trajectory tracking control 88–147
control 414–415
localization and mapping 414
path planning 415–416
sensors 412–414
Wheel odometry 442–443
kinematic model of 436
White noise 299–300
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