索引

Index

Note: Page numbers followed by f indicate figures, t indicate tables and b indicate boxes.

A

A* algorithm 199–201, 200f, 202–205b

Absolute sensors 207

Ackermann steering mechanism 24–26, 25f

Active markers 241, 251

Adaptive and Generic Accelerated Segment Test (AGAST) 277

Aerial mobile systems 4

Artificial markers 269–270, 276, 276f, 277f

Autonomous guided vehicles (AGVs) 

transportation vehicles 

control 392

decision making 392

localization and mapping 390–391

path planning 392

sensors 389–390

wheeled mobile system 

in agriculture 393–398

in domestic environments 403–410

in industry 399–403

in walking rehabilitation therapy 410–416

Autonomous mobile systems 

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

Ball motion 454, 455f

Bayesian filter 

environment sensing 320–325

localization 

in environment 332–337

principle 318–320

Markov chains 304, 304f, 305f

motion, in environment 326–332

state estimation 297–299b, 304–318, 306b

Bayesian theorem 312

Bayes’ rule 295–296b, 295–299

Behavior-based agent operation 458–459

Belief 313–314, 430, 432f

Bicycle drive kinematics 20–23, 21f

Binary images 268–269, 269f, 271–273

Breadth-first search algorithm 193–194, 193f

Bridge test 185–186, 186f

Brockett’s condition 91–92

Bug0 algorithm 187, 187f, 190–191b

Bug1 algorithm 188–189, 188f

Bug2 algorithm 189–192, 189f

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

Conditional probability 291, 295–296b, 313–314

Configuration space 163–164

Continuous random variable 290–295, 292f, 293t

Control algorithm 

for differentially driven robot 85–87b, 87f

for differential type robot 73–74b, 74f, 76–78b, 77f, 78f

phases 72, 76

reference pose 

intermediate direction 75–78, 75f

intermediate point 72–74, 72f

to reference path control 83–87, 84f

on segmented continuous path, by line and circle arc 78–83, 79f

for tricycle robot 68–69b, 69f

Control error 63

Controllability 44–47

Controller gains 102–103

Coordinate frame transformations 

orientation and rotation 208–217

projective geometry 221–230

rotating frames 219–221

translation and rotation 217–218

Cost-to-goal 199

Covariance matrix 259–260, 347–348, 375

D

DCM  See Direction cosine matrix (DCM)

Dead reckoning 17–18, 231–239

inertial navigation system 232–239

odometry 231–232

Decomposing control 

feedback action 91–94

feedforward 91–94

Decomposition to cells 

accurate decomposition 167–168, 167f

approximate decomposition 168–172, 168f, 169f

Delaunay triangulation 176, 176b, 177f

Depth-first search algorithm 194, 195f

Differential drive kinematics 15–20, 16f

parallel parking maneuver 37–38b, 38f

reachable velocities and motion constraints 36b

Differentially driven wheeled mobile robot 

PDC control of 125

Takagi-Sugeno fuzzy error model of 122–124

Dijkstra’s algorithm 195–198, 198f

Directed graph 166, 166f

Direction cosine matrix (DCM) 208–209, 211–212, 220–221

Direct kinematics 14, 17–18

Discrete probability distribution 291, 292f

Discrete random variable 290–291, 292f, 293, 293t

Distribution 

definition 35

involutive 36

Dynamic constraints 32–33

Dynamic environment 161–162

Dynamic models 13, 56b

E

Environment sensing 320–325

Epipolar constraint 224–225

Essential matrix 224–225

Estimate convergence and bias 300–301

Euclidean distance 188, 188f, 189

Euler angles 209–210

Euler integration method 421–423

Extended Kalman filter (EKF) 351–374

External kinematic model 232

Exteroception (EC) 285t, 287

Exteroceptive sensors 286

F

Feedback action 91–94

Feedback control part 61

Feedback linearization 94–99, 97f

control law 94, 96

differentially driven vehicle 97–99b, 99f

flat outputs 94

for reference tracking 96, 97f

reference trajectory 95–96

state-space representation 95–96

translational velocity 95–96

Feedforward 91–94

Feedforward control part 61, 79

Flat outputs 92–93

Forward-motion control 

control algorithm 68–69b, 68

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

Global navigation satellite system (GNSS) 389, 390f, 392, 396, 396f, 433

GNSS  See Global navigation satellite system (GNSS)

GNSS signal receiver 389, 390f, 391

Goalie motion control 457–458, 457f

Graph-based path planning methods 

A* algorithm 199–201, 200f

breadth-first search algorithm 193–194, 193f

depth-first search algorithm 194, 195f

Dijkstra’s algorithm 195–198, 198f

greedy best-first search algorithm 201–205, 202f

iterative deepening depth-first search 194–195, 196f

Greedy best-first search algorithm 201–205, 202f

Ground mobile systems 4

H

Heading measurement systems 239–240

Hidden Markov process 304, 304f, 305f

Histogram filter 375

Holonomic constraints 32–34

Hough transform 265–268, 267f

Hue-saturation-lightness (HSL) 270–271

Hue-saturation-value (HSV) 270–271, 271f

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

line-following and crossroad-detection 421, 422f

INS  See Inertial navigation system (INS)

Instantaneous center of rotation (ICR) 14

Internal camera model 222–223

Internal kinematic model 13–14, 231–232

Inverse kinematics 14, 17–20

Iterative deepening depth-first search 194–195, 196f

J

Jacobian matrix 352–353, 436–439

Joint probability 290

K

Kalman-Bucy filter 375

Kalman filter 234–235, 337–375, 339–340b

algorithm 343, 343b

continuous variable, probability distribution of 338f

correction covariance matrix 348

correction step 338, 343–344

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

Ackermann steering principle 24–26, 25f

bicycle drive 20–23, 21f

differential drive 15–20, 16f

direct kinematics 14, 17–18

external kinematics 14

instantaneous center of rotation (ICR) 14

internal kinematics 13–14

inverse kinematics 14, 17–20

motion constraints 14

omnidirectional drive 27–31

synchronous drive 26–27, 26f

tracked drive 31–32

tricycle drive 23, 23f

tricycle with a trailer 23–24, 24f

Kinematic trajectory-tracking error model 100–101, 100f

L

Laser range finder (LRF) 254–255

Laser range scanner (LIDAR) 389–390, 390f

Least squares method 254

Lie brackets 36

Lie derivative operator 302–303

Linear controller 101–105, 103–105b, 104f

Linear formation, vehicle control 

laser range finder (LRF) 447–448

localization, using odometry 448–449

reference trajectory estimation 449–451, 450f

vehicle path 447–448, 448f

virtual train formation 447

Linear matrix inequality (LMI) 122

Line-extraction algorithms 254

Line following algorithm 420–421, 422f

Localization 332

algorithm 318–319

Bayesian filter 

line following 420–421, 422f

map building 423–426, 424f, 425f, 425t

odometry 421–423, 424f

in environment 332–337

extended Kalman filter (EKF) 

correction step 438–439

global navigation satellite system (GNSS) 433

prediction step 435–438

particle-filter-based 

colored tiles 440–447

color sensor calibration 443–444, 444f

evolution of 445, 445f

manual control 442

ultrasonic distance sensors 467–471, 467f

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

stability 

control design in 106–112

Lyapunov functions 106, 113

Lyapunov stability 106

control design in 106–112

M

Machine vision algorithms 279–280

Manhattan distance 199–200

Manual control 442

Map building method 423–426, 424f, 425f, 425t

Maps 282–283

Markov chains 304, 304f, 305f

Maximally Stable Extremal Regions (MSER) 277–278

Memory usage 168–169

Mobile 2–3

Mobile robot 

color sensor, mounting of 440–441, 441f

image-based control of 

image-based visual servoing (IBVS) 465–466

natural image features 466

position-based visual servoing (PBVS) 463–465

path planning of 

A* path searching algorithm 472–475, 473f, 474f, 475f

distance transform, visualization of 479–480, 480f

optimum path, in map 475–481, 478f, 479f

Model-based predictive control (MPC) 125–132, 130–132b, 130f, 131f

Motion 

constraints 

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

control 

attacker 453–455, 454f

straight accelerated kick 455–457, 456f

in environment 326–332

sensors 232–233

Motion models 

constraints 

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

dynamic motion model with constraints 

differential drive vehicle 51–58

Lagrange formulation 48

meaning of matrices 48, 49t

state-space representation 49–50

kinematic model 

Ackermann steering principle 24–26, 25f

bicycle drive 20–23, 21f

differential drive 15–20, 16f

direct kinematics 14, 17–18

external kinematics 14

instantaneous center of rotation (ICR) 14

internal kinematics 13–14

inverse kinematics 14, 17–20

motion constraints 14

omnidirectional drive 27–31

synchronous drive 26–27, 26f

tracked drive 31–32

tricycle drive 23, 23f

tricycle with a trailer 23–24, 24f

two-wheeled cart 13, 14f

MPC  See Model-based predictive control (MPC)

Multiagent game strategy 459–460, 461f, 462f

Multiagent soccer robots 

attacker motion control 453–455, 454f, 455f

behavior-based agent operation 458–459

goalie motion control 457–458, 457f

multiagent game strategy 459–460, 461f, 462f

obstacle avoidance 458

setup 452, 453f

straight accelerated kick motion control 455–457, 456f

Multiview geometry 224–228

N

Natural local image features 269–270

Nodes 

direct path shapes 425–426, 425f, 427f, 429f

ending 423–425, 425t

paths in 423–425, 425t

start 194, 197

Noise linearization 352

Noise modeling 439

Nondeterministic events, in mobile systems 

Bayesian filter 

environment sensing 320–325

localization, in environment 332–337

localization principle 318–320

Markov chains 304, 304f, 305f

motion, in environment 326–332

state estimation 304–318

Kalman filter 

derivatives 374–375

extended 351–374

in matrix form 345–351

particle filter 375–386, 378f

probability 

Bayes’ rule 295–299

continuous random variable 291–295, 292f, 293t

discrete random variable 290–291, 292f, 293t

state estimation 

disturbances and noise 299–300

estimate convergence and bias 300–301

observability 301–303

Nonholonomic constraints 32–35, 61, 63, 92–93

Noninformed algorithms 193

Nonlinear tracking error model 123–124

Normal distribution 293–294, 294f

O

Objective function 132–133

Observability 301–303

analysis 358, 359f, 368f

rank condition 302–303

Obstacle avoidance 458

Occupancy grid 168

Odometry 17–18, 231–232, 421–423, 424f

drifting 443

Omnidirectional drive 

four-wheel Mecanum drive 28–30

Mecanum wheel or Swedish wheel 27–28, 27f

three-wheel Mecanum drive 30–31, 31f

Open list 192

Optimal velocity profile estimation 148–157, 152–155b

friction force 148–149

radial acceleration 148–150

reference path 148

reference trajectory 151

tangential acceleration 148–150

velocity profile 148

Orientation and rotation 

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

Particle filter 375–386, 379–382b, 383–384b, 444–447

algorithm 376–377b, 376

Bayesian filter 375

in correction part 377, 378f

evolution of 445, 445f

Gaussian probability distribution 377

in prediction part 377

probability distribution 376

Particle measurement prediction 

from known robot motion 468

sensor model for 468–470, 469f

Particle swarm optimization-based control (PSO) 132–141, 134–137b, 137f, 138–141b, 242–243, 244f

code 133–134b, 133

model predictive control (MPC) 138–141

Path planning 

cells, decomposition to 166–172, 166f, 168f, 169f

configuration and configuration space 163–164, 164f

graph-based path planning methods 

A* algorithm 199–201, 200f

breadth-first search algorithm 193–194, 193f

depth-first search algorithm 194, 195f

Dijkstra’s algorithm 195–198, 198f

greedy best-first search algorithm 201–205, 202f

iterative deepening depth-first search 194–195, 196f

graph representation 166, 166f

obstacle shape and pose, in environment 164–165, 165f

potential field method 177–180, 178f

roadmap 

triangulation 176–177, 177f

visibility graph 172–173, 173f

Voronoi graph 173–176, 173f, 174f

robot environment 161–162, 162f

sampling-based path-planning 

probabilistic roadmap (PRM) 184–186, 184f, 185f

rapidly exploring random tree (RRT) 182f, 182–184, 183f

simple path planning algorithms 

Bug0 algorithm 187, 187f

Bug1 algorithm 188–189, 188f

Bug2 algorithm 189–192, 189f

Periodic control law design 112–117

Pinhole camera model 222–223, 222f

Pole placement approach 102–103

Pose error 233

Pose measurement methods 

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

Posterior state probability distributions 324, 325f

Posture error 100–101, 100f

Potential field method 177–180, 178f, 179b

Probabilistic roadmap (PRM) 184–186, 184f, 185f, 186b

learning phase 184–185, 184f

path searching phase 184f, 185

Probability 

Bayes’ rule 295–299

continuous random variable 291–295, 292f, 293t

discrete random variable 290–291, 292f, 293t

Probability density function 291–292, 292f

Probability distribution 326–328b, 305

Projective geometry 221–230

3D reconstruction 228–230

internal camera model 222–223

multiview geometry 224–228

parallel projection 221

perspective projection 221

pinhole camera model 222–223, 222f

singular cases 228

Proportional controller 85

Proprioception (PC) 285t, 287

Proprioceptive sensors 286

PSO  See Particle swarm optimization-based control (PSO)

Q

Quaternions 210–217, 219–220

R

Radio-controlled electrical boat 3, 3f

Random Sample Consensus (RANSAC) method 280–282

Rapidly exploring random tree (RRT) 182–184, 182f, 183b, 183f

Reference orientation 84–85

Reference pose, control to 

forward-motion control 66–70

intermediate direction, using an 75–78, 75f

intermediate point, using an 72–74, 72f

orientation control 

for Ackermann drive 65–66

for differential drive 63–65

reference path control 83–87, 84f

reference position 70–71

segmented continuous path, by line and circle arc 78–83, 79f

Reference trajectory 126

estimation 449–451, 450f

Relative sensors 207

RGB color model 270–271, 271f

Roadmap 

triangulation 176–177, 177f

visibility graph 172–173, 173f

Voronoi graph 173–176, 173f, 174f

Robot(s) 1–2

Robot environment 

configuration 163–164, 164f

configuration space 163–164, 164f

free space 161–162

obstacles 161–162, 162f

border-based description of 165, 165f

intersecting half-planes 165, 165f

path planning purposes 

cells, decomposition to 166–172, 167f, 168f

graph representation 166, 166f

potential field 177–180, 178f

roadmap 172–177, 173f, 174f

sampling-based path-planning 180–186, 181f

start and goal configurations 161–163, 162f

Robot kidnapping 447

Robot soccer test bed 452, 453f

Robot-tracking prediction-error vector 128

Rossum’s Universal Robots (R.U.R.) 1

Rotation matrix 

Matlab implementations 

rotX function 226–228b, 237

rotY function 226–228b, 237

rotZ function 237

orientation and rotation 208–217

translation and rotation 217–218

Rotation sensors 232–233

RRT  See Rapidly exploring random tree (RRT)

S

Sampling-based path-planning 

probabilistic roadmap (PRM) 184–186, 184f, 185f

rapidly exploring random tree (RRT) 182–184, 182f, 183f

Scale invariant feature transform (SIFT) 277

Self-contained technique 232–233

Sensors 289

absolute sensors 207

characteristics 284–286

classifications 286–287

coordinate frame transformations 

orientation and rotation 208–217

projection 221–230

rotating frames 219–221

translation and rotation 217–218

pose measurement methods 

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

Simple path planning algorithms 

Bug0 algorithm 187, 187f

Bug1 algorithm 188–189, 188f

Bug2 algorithm 189–192, 189f

Simultaneous localization and mapping (SLAM) 253

Smooth time-invariant feedback 91–92

Speeded-Up Robust Features (SURF) 277

Speedup algorithms 271–273

Split-and-merge algorithm 255–259, 256f

State error covariance matrix 437–438, 437f

State estimation 

Bayesian filter 

correction step 307, 313

general algorithm for 313–318, 314b

from observations and actions 311–318

prediction step 306–307, 313

disturbances and noise 299–300

estimate convergence and bias 300–301

observability 301–303

State probability 307–311b, 307

State transition graph 166

Static environment 161–162

Stochastic optimization 132–133

Straight accelerated kick motion control 455–457, 456f

Straight-line clustering algorithm 259–265, 262f

Straight-line features 254–268

Synchronous drive 26–27, 26f

T

Takagi-Sugeno fuzzy control design 122–125

3D reconstruction, stereo camera configuration 228–230

Total probability theorem 313

Tracked drive 31–32, 32f

Trajectory tracking control 

feedback action 91–94

feedback linearization 94–99, 97f

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

Triangulation/trilateration approach 176–177, 177f, 241, 241f, 242

Tricycle drive kinematics 23, 23f

Tricycle robot 

control algorithm for 68–69b, 69f

wheels, rear-powered pair of 88–91b, 89f, 90f

Two-degree-of-freedom control 61, 91–92

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

Visibility graph 172–173, 173f

Visual servoing (VS) approaches 142–147, 145–147b, 146f, 147f

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

Voronoi graph 173–176, 173f, 174–176b, 174f, 175f

in Matlab 174–176b

roadmap 173, 173f

Voronoi curve 173, 174f

W

Water and underwater mobile systems 4

Weighted graph 166, 166f

Wheeled mobile system 

in agriculture 

control strategies 397–398

localization, mapping, and slam 397

planning routes and scheduling 398

service unit setup 394–397

in domestic environments 

control 408–409

decision making 409–410

localization and mapping 407

path planning 408

sensors 405–407

in industry 

control 402–403

decision making 403

localization and mapping 401–402

path planning 403

sensors 401

motion control of 

optimal velocity profile estimation 148–157

to reference pose 62–87

trajectory tracking control 88–147

in walking rehabilitation therapy 

control 414–415

localization and mapping 414

path planning 415–416

sensors 412–414

Wheel odometry 442–443

kinematic model of 436

Wheels 3–4, 4f

White noise 299–300

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