Index

(t,m,s)-nets
Low-discrepancy
(t,s)-sequences
Low-discrepancy
1-complex
Homeomorphism:
1-neighborhood
Neighborhoods | Other
2-neighborhood
Neighborhoods
3D triangles
3D to 3D
acceleration vector
8.4.4.1
acceleration-based control
8.4.4.1 | 8.4.4.1 to 8.4.4.1
accelerometer
Simple
accessible system
STLC: | 15.4.3.5 to 15.4.3.5
accumulation point
Special
Ackerman function
6.5.2 | 6.5.2
action history
8.4.1.1 | History
action sequence
14.2.2
action trajectory
8.4.1.1 | 14.1.1
active localization problem
12.2.1
active-passive decomposition
7.4.2 | Active
actuators
Underactuation
Multistep
13.2.4 to 13.2.4.3
15.2.3
13.4.4 | 15.2.3
affine space
Varieties
airport terminal
algebraic primitive
3.1.2 | 3.1.2 | 3.1.2 | 3.1.2 | 3.1.2 | Some | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3
algebraic Riccati equation
15.2.2
algebraic set
3.1.2
alive states
2.2.1 | 2.3.3 | 2.3.3 | 8.5.2.3
Allen wrench
12.5.2
Alpha Puzzle
A | A
alphabet
11.3.2
Amato
A | A
ambient isotopy
Simplifying
ambient space
Simplifying
analytic function
Vector
angular velocity
Simple | 13.1.2.2 | 13.1.2.2 | 13.1.2.3 | The | The | Differential | Inertia | The | The | A | A
annihilator
15.4.2.2
antipodal points
Higher
approximate cell decomposition
Further
approximate cover
8.5.1.1 to 8.5.1.1
approximate optimal motion planning
General to General
approximation algorithm
Approximation | Approximation to Approximation
arrangement
Further
Asimo
Virtual | Virtual
assembly planning
Assembly to Assembly
asteroids game
2D
asymptotic convergence to a goal
8.4.1.1
asymptotic solution plan
8.4.1.1
asymptotic stability
Asymptotic | Asymptotic to Asymptotic
atan2
Determining
automated farming
7.6
automated guided vehicles
automotive assembly
An
autonomous differential equations
Vector
average cost-per-stage model
10.3 | Average | Average to Average
average dispersion
Exercises
averaging methods
Averaging | Averaging to Averaging
axioms of rationality
9.5.1.2 | 9.5.1.2 to 9.5.1.2
axis-aligned bounding box
5.3.2 | 5.3.2
B-splines
Nonuniform to Nonuniform
backprojection
8.5.2.3 | Backprojections to Backprojections | 14.5.2 | 14.5.2 to 14.5.2 | 14.6.3.4
in preimage planning
Backprojections to Computing
backward action space
Backward
backward P. Hall coordinates
The | Returning | Returning | The | The | Using
backward reachable set
14.2.1.3 | 14.2.1.3 to 14.2.1.3
backward search
Backward to Backward | 5.4.1 | Computing | Planning | Computing | Squeezing
with backprojections
Backward to Backward
backward state transition equation
Backward | 2.3.1.2 | 2.3.2
backward system simulator
Reverse-time
backward value iteration
2.3.1.1 to 2.3.1.1
for reinforcement learning
Value to Value
for sequential games
Value to Value
on a nondeterministic I-space
Value to Value
on a probabilistic I-space
Approximate to Exact
path-constrained
14.6.3.4 to 14.6.3.4
running time
2.3.1.1
under differential constraints
14.5.2 to 14.5.2
with average cost-per-stage
Solutions to Solutions
with discounted cost
Value to Value
with nature and continuous spaces
10.6.1 to 10.6.1
with nondeterministic uncertainty
Nondeterministic to Nondeterministic | Convergence to Using
with probabilistic uncertainty
Probabilistic to Using
15.4.3.5
Balkcom-Mason curves
15.3.3 | 15.3.3 to 15.3.3
Balkcom-Mason drive
15.3.3
Balkcom-Mason metric
15.3.3
bang-bang approach
14.6.3.5 | 14.6.3.5 to 14.6.3.5
Barraquand-Latombe nonholonomic planner
14.4.2 to Backward
base point (on a manifold)
15.4.2.2
base point of a path
The
basis
Vector
of open sets
Some
6.4.3
Battle of the Sexes
9.4.1.1 to 9.4.1.1
Battleship game
11.7.2 to 11.7.2
Bayes' rule
Conditional | Marginalization | A | 11.2.3 | Discrete
Bayesian classifier
A | A to A
naive
A
behavioral strategies
11.7.1
best-first search
Best | Best to Best
bidirectional search
Bidirectional to Bidirectional | Further | Expansive-space | Exercises | The | 14.2.1.3 | 14.3.4 | 14.3.4 | 14.3.4 | Backward | Backward | Tree-based
balanced
Balanced, to Balanced,
for sampling-based planning
5.4.1
bijective sensor
11.1.1 | 11.1.1 to 11.1.1
bilinear programming
9.4.1.2
binding constraints
2.5.1
bitangent line
6.2.4
bitangent ray
Critical
bitmap
Bitmaps to Bitmaps
black-box simulators
Black-box to Black-box
Blum and Furst
2.5.2
Blum and Kozen
Algorithms | Algorithms
body density
13.3.3
body frame
Defining | 4.4.3 | 4.4.3 | Drug | Drug | Exercises | Important | Simplifying | Simplifying | Completing | The
bond angle
The
bond length
The
Borel sets
5.1.3 | 5.1.3
boundary grid point
Discretization
boundary of a set
Special
boundary point
Special
boundary representation
3.1
boundary sensors
Boundary to Boundary
bounded set
Homeomorphism:
bounded-acceleration model
8.4.4.2
bounded-velocity model
8.4.4.2
Boustrophedon decomposition
Boustrophedon to Spanning
Brachistochrone curve
13.4.1.1
bracket
15.4.3.1
bridge-test sampling
Bridge-test to Bridge-test
Two-phase
Brockett
13.2.3 | 15.5.2 | 15.5.2.2
Brockett's condition
Time-varying
bug algorithms
12.3.3 | 12.3.3 to Competitive
bug trap
5.4.1
Bug1 strategy
The | The to The
Bug2 strategy
The | The to The
caffeine
Designing | Designing
calculus of variations
9.1.1.1 | 13.4.1.1 | 13.4.1.1 to 13.4.1.4 | Variational to Variational
Campbell-Baker-Hausdorff-Dynkin formula
The
candidate Lyapunov function
Determining
9.5.1.2
Canny
6.4.3
6.4.3 to 6.4.3 | Further | Combinatorial | Fixed-path | Combinatorial
car pulling trailers
Parking | 13.1.2.4 to 13.1.2.4
Caratheodory, solution sense of
An
card-counting strategies
Exercises
Carnot-Caratheodory metric
The
Cartesian product
Cartesian
carton folding
Carton to Carton
2.5.1
CBHD formula
The
cell decomposition
6.2 | 6.3 | 6.3.1 | Simplicial to Complexity | 12.2.2 | 12.4.3
under differential constraints
Decomposing to Decomposing
center of mass
13.3.3
Central Limit Theorem
Generating
chain of integrators
13.2.1.3 | 13.2.1.3 to 13.2.1.3
chained-form system
15.4.3.2 | 15.5.2.3 | 15.5.2.3 to 15.5.2.3
change of coordinates
Coordinates | Coordinates to Coordinates
chasing a gap
Critical
Chazelle
Warning:
Chen-Fliess series
The | The to The | Returning to Returning
Chen-Fliess-Sussman equation
The to The
chi-square test
Testing
Chow-Rashevskii theorem
15.4.3.4
Christoffel symbol
13.4.2 | 13.4.2
Church-Turing thesis
1.4.1
classification rule
9.2.4.1
classifier
9.2.4.1 | 9.2.4.1 to A
cleared region
12.4.2
closed kinematic chains
What | 4.4 to 4.4.3
motion planning for
7.4 to Computing
closed set
Closed
closed system (in mechanics)
Closed to Closed
closed-loop
control law
Open-loop
plan
8.1
closure of a set
Special | Denseness
closure space
Closure
codistribution
15.4.2.2
coherent models
5.3.3
collision detection
5.3 to 5.3.4
Two-phase
checking a path segment
5.3.4 to 5.3.4
hierarchical methods
5.3.2 to 5.3.2
incremental methods
5.3.3 to 5.3.3
narrow-phase
Two-phase
two-phase
Two-phase to Two-phase
collision pairs
Obstacle
collision-detection
14.3.1.3 to 14.3.1.3
collocation
14.7
combinatorial motion planning
6. to Specialized
cell decompositions
6.3 to Complexity
introductory concepts
polygonal case
6.2 to 6.2.4
5.6
commutative group
Groups | 15.4.2.3
commutative ring
Polynomials
commutator
15.4.2.3
commutator motion
15.4.2.3 to 15.4.2.3 | 15.5.1
compass
Simple | 12.2.2
compatible coordinate neighborhoods
Coordinates
competitive ratio
Landmark | Competitive | Competitive to Competitive
complementary pair
2.4.1
complete exclusion axiom
2.5.3
completely integrable
13.1.3.4 | 15.4.2.1 | 15.4.2.1 to 15.4.2.1
completeness
overview
Notions to Notions
complex
6.3.1 | 6.3.1 | 6.3.1 to Singular
complexity class
Languages
complexity of motion planning
6.5 to Specialized
lower bounds
6.5.1 to Lower
upper bounds
6.5.3 to Specialized
compliant motions
12.5.1 | Compliant | Compliant to Compliant | Backprojections
composition of funnels
8.5.1 to Termination | Squeezing | Domains
compressed mode
7.3.1
computational algebraic geometry
6.4 to 6.4.3
Conchoid of Nicomedes
Critical | Exercises
conditional Bayes' risk
Optimal
conditional Bayes' rule
Conditional
conditional expectation
Expectation
conditional independence
Conditional
conditional probability
Conditional | Conditional to Conditional
configuration space
4. | 4. | 4.2 to 4.4.3
of 2D rigid bodies
4.2.1 to Interpreting
of 3D rigid bodies
4.2.2 to Special
of chains of bodies
4.2.3 to 4.2.3
of trees of bodies
4.2.3 to 4.2.3
velocity constraints on
13.1 to 13.1.3.4
conformations
The | Drug
connected space
Connected
5.6.2
conservative approximations
11.4.3.2 | Conservative to Conservative
conservative system
13.4.1.2
constant vector field
Vector
constant-sum game
Exercises
contaminated region
12.4.2
Euclidean
continuous function
Continuous
continuous-steering car
13.2.4.2 | 13.2.4.2 to 13.2.4.2
contractible space
The
control system
13. | Open-loop
control-affine system
13.2.3 | 15.4.1 to 15.4.1
controllability matrix
Classical
controllability of a system
15.1.3 to STLC:
linear case
Classical to Classical
controlled Markov process
10.1.1
convex hull
5.3.2 | 5.3.2 | Piecewise-smooth
convex polygon
Convex to Convex
convex set
Convex
convolution
4.3.2
cooperative game theory
Further
coordinate neighborhood
Coordinates
coordinates
Coordinates
coordination space
Fixed-path | Fixed-path
Coriolis matrix
13.4.2
cost functional
2.3 | General | 7.7.2 | 10.1.1 | Discounted | 11.7.2 | 14.5.2
approximating
Approximating
15.2.2
cost-to-come
Dijkstra's | 2.3.1.2 | 2.3.1.2 to 2.3.1.2 | 14.2.1.2
cost-to-go
A-star | 2.3.1.1 to 2.3.1.1 | General | Feasibility | Navigation | Navigation | Computing | Wavefront | Dial's | 8.4.1.2 | 8.4.3 | 8.4.3 | 8.4.3 | 8.4.3 | 8.4.3 | 8.5.1 | 8.5.2.1 | 8.5.2.1 | Continuous | 8.5.2.2 | Obtaining | Handling | Using | 8.5.2.3 | 8.5.2.3 | 8.5.2.3 | 8.5.2.3 | 8.5.2.3 | 10.6.1 | The | The | Distance | Distance | 14.5.2 | 14.5.2 | 14.5.2 | 14.5.2 | 14.5.2 | 14.5.2 | 14.6.3.4 | 14.6.3.4
Coulomb friction
Compliant
counting measure
5.1.3 | 5.1.3 to 5.1.3
covariance matrix
Moment-based
cover of a set
8.5.1.1
approximate
8.5.1.1
coverage planning
7.6 | 7.6 to Spanning
Coxeter-Freudenthal-Kuhn triangulation
8.5.2.1
critical curves
Critical
critical gap events
Critical to Critical
critical point of a function
6.4.3 | 8.4.4.3
cube complex
Cube | Cube | Cube to Planning
cubical partition
Decomposing
CW-complex
6.3.1
cycloid function
13.4.1.1
cylinder over a cell
Cylindrical | Critical | The
cylindrical algebraic decomposition
6.4.2 to Solving | Combinatorial | Fixed-path
for motion planning
Solving to Solving
cylindrical decomposition
Cylindrical | Cylindrical to Cylindrical
D'Alembert
13.4.3.1
Davenport-Schinzel sequence
6.5.2 | 6.5.2 to 6.5.2
Davis-Putnam procedure
2.5.3
2.2.1 | 2.2.1 | Dijkstra's | Dijkstra's | 8.5.2.3
decision maker
1.1 | 9.
decision problem
The | The to The
decision theory
9.
decision vertex (in a game tree)
10.5.1
decoupled planning
Reasons | 14.6 to 14.6.3.5
decoupling vector fields
14.6.3.2 | Decoupling | Decoupling to Decoupling
deformation retract
6.2.3
degrees of freedom
Translation
delayed-observation sensor
11.1.1 | 11.1.1 to 11.1.1
Denavit-Hartenberg parameters
3.3.2 | 3.3.2 to Two | The | The | 4.2.2 | 4.2.3 | Chains | 4.4.3
dense sequence
Denseness | 14.2
dense set
Denseness | Denseness | Denseness
dependent events
Conditional
depth-first search
Depth | Depth to Depth
depth-mapping sensors
Depth-mapping to Depth-mapping
derivation (on a manifold)
Tangent
derived information space
11.2 | 11.2.1 to Sensor | 11.4.3 to 11.4.3.3
for continuous time
11.4.3.3 to 11.4.3.3
derived information transition equation
Constructing
determining the environment
12.3.1 to 12.3.1 | 12.3.1 | Algorithms to Algorithms
deterministic finite automaton
2.1.2 | 11.3.2
language
2.1.2
deterministic plan
10.5.1 | Defining | 11.7.1
Dial's algorithm
Dial's to Dial's | Other
diameter function
Squeezing
dielectric constant
Drug
diffeomorphic spaces
Smoothness
diffeomorphism
Smoothness
differential drive
15.4.3.4
model
13.1.2.2 to 13.1.2.2
second-order
13.2.4.3
showing it is nonholonomic
15.4.2.4 to 15.4.2.4
differential game
13.5.2 | 13.5.2 | 13.5.2 to 13.5.2
against nature
13.5.1
pursuit-evasion
13.5.2
differential inclusion
Piecewise-smooth | 13.5.1
differential models
13. to 13.5.2
conversion from implicit to parametric
13.1.1.3 to 13.1.1.3
implicit representation
13.1.1.1 to General
parametric representation
13.1.1.2 to 13.1.1.2
differential rotations
Differential to Differential
differentially flat systems
Differentially to Differentially
digital actor
Virtual | Virtual | Virtual
Dijkstra's algorithm
2. | Dijkstra's to Dijkstra's | 2.3.3 to 2.3.3 | Computing | Computing | Wavefront | Wavefront | Wavefront | Dial's | 8.4.2 | 8.4.3 | 8.4.3 | 8.5.2.3 | 8.5.2.3 | 8.5.2.3 | 8.5.2.3 | 10.6.1 | 12.3.2 | 12.3.2 | 12.3.2 | 14.4.1.1 | 14.5.2 | 14.5.2 | 14.6.3.4
extension of to continuous spaces
8.5.2.3 to 8.5.2.3
with nondeterministic uncertainty
Nondeterministic to Nondeterministic
with probabilistic uncertainty
Probabilistic to Probabilistic
dimension
of a manifold
Manifold
of a vector space
Vector
Sampling-based
Dirichlet boundary condition
8.4.4.4
disconnection proof
Further
discount factor
Discounted
discounted cost model
10.3 | Discounted | Discounted to Discounted
discrepancy
5.2.4 to Low-discrepancy | 14.3.1.2 to 14.3.1.2
range space
5.2.4
relation to dispersion
Relating
discrete feasible planning
2.1.1
discrete-time model
14.2.2 | 14.2.2 to 14.2.2.3
dispersion
5.2.3 | Dispersion to Dispersion | 8.5.2.1 | 14.3.1.2 to 14.3.1.2
relation to discrepancy
Relating
distance between sets
Distance to Distance
distance function
Distance
distribution (of vector fields)
15.4.2.2 to 15.4.2.2
regular
15.4.2.2
singular
15.4.2.2 to 15.4.2.2 | 15.4.2.2
disturbed odd/even sensor
11.1.1
disturbed sign sensor
11.1.1 to 11.1.1
domain of attraction
Domains | Domains to Domains
dominated action
9.1.1.2
dominated plan
7.7.2
Donald
Further | Backprojections
double integrator
13.2.1.1 | 13.2.1.1 to 13.2.1.1 | 13.2.4.3 | 13.3.2.1 | The | 14.1.1 | Kinodynamic | 14.1.3.2
lattice
14.4.1 to Underactuated
optimal planning for
15.2.3 to 15.2.3
doubly connected edge list
Polyhedral | 6.2.1 | 6.2.1 | Algorithm | Algorithm | Algorithm
drift
13.2.1.3 | 13.2.3 | Drift | 15.4.1
driftless
13.2.3 | Drift
driftless system
13.2.1.3 | 15.4.1
controllability
15.4.3.4 to 15.4.3.4
drug design
Designing | Drug to Drug
Dubins car
13.1.2.1 | 13.5.2 | Symmetric | 14.1.3.2 | 14.2.1.2 | 14.2.1.2 | 14.2.1.2 | 14.2.1.2 | 14.2.2.1 | 14.2.2.1 | 14.2.2.3 | The | 14.3.3 | 14.4.2 | Searching | Searching | Resolution | Resolution | Resolution | Distance | 14.5.1 | 14.6.1 | 14.6.2 | 14.6.2 | 14.6.2 | 14.6.3.2
plan-and-transform approach
14.6.2 to 14.6.2
reachability tree of
14.2.2.1 to 14.2.2.1
Dubins curves
15.3.1 | 15.3.1 to 15.3.1
Dubins metric
15.3.1
dynamic constraints
15.4.1
dynamic programming
2.
applied to steering
Dynamic to Dynamic
continuous-time
15.2 to Time
dynamics
of a particle
13.3.2 to 13.3.2.1
of a rigid body
13.3.3 to A
of a set of particles
13.3.2.2 to 13.3.2.2
13.4.2.1 to 13.4.2.1
of chains of bodies
13.4.2 to 13.4.2.1
of constrained bodies
13.4.3.1 to 13.4.3.1
with nonconservative forces
13.4.3.2 to 13.4.3.2
efficient algorithm
Languages | Lower | General
elongated mode
7.3.1
EM algorithm
The to The
embedding of a manifold
Manifold
energy function
7.5 | Simplifying | Drug
equilibrium point of a vector field
Equilibrium
Erdmann
Backprojections | Backprojections | Backprojections
error detection and recovery (EDR)
Backprojections
Euclidean metric
Euclidean motion model
General to General
Euclidean norm
Euclidean shortest paths
Euclidean to Euclidean
Euler angles
Further
Euler approximation
Obtaining
Euler-Lagrange equation
13.4.1.1 | 13.4.1.1 | 13.4.1.2 | 13.4.1.2 | 13.4.1.2 | 13.4.1.3 | 13.4.1.4 | 13.4.1.4 | 13.4.2 | 13.4.2 | 15.2.3 | 15.4.1 | 15.5.2.1 | Pontryagin's | Pontryagin's | Pontryagin's
with conservative forces
13.4.3.2
event space
Probability
exit face
8.4.2
expansive-space planner
Expansive-space to Expansive-space
expectation of a random variable
Expectation to Expectation
expected-case analysis
9.2.2 | The | The
exploration vs. exploitation
The
exponential map
The to The
exponentially stable system
Asymptotic
EXPTIME
Languages
extended Kalman filter
11.6.1 | Continuous
extended system
15.5.1
exterior point
Special
extremal function
13.4.1.1
falling particle
13.4.1.2 to 13.4.1.2
fast Fourier transforms
Handling
Faure sequence
Low-discrepancy
feasible planning
discrete
2.1.1
with feedback
Feasibility to Feasibility
feasible space (for closure constraints)
Closure
feature space
9.2.4.1
feature vector
9.2.4.1 | A | A | A
feedback motion planning
complete, optimal
8.4.3 to 8.4.3
complete, some dynamics
8.4.4 to 8.4.4.4
definitions
8.4.1 to 8.4.1.2
motivation
8.1 to 8.1
sampling-based
8.5 to 8.5.2.3
under differential constraints
14.5 to 14.5.2
feedback plan
8.2.1 | 8.2.1 to 8.2.1 | Defining | Defining to The
cost of
The to The
graph representation of
Graph to Graph
information feedback
11.1.3 to 11.1.3
over a cover
8.5.1.2 to 8.5.1.2
sensor feedback
Sensor
feedback planning
discrete
8.2 to Other
feedback stabilization
15.1.1
fiber over a base
15.4.2.2
fictitious action variable
15.5.1
field
Fields | Fields | Fields to Fields
algebraically closed
Real
Filipov, solution sense of
Piecewise-smooth | Vector
finite state machine
2.1.2
firetruck
13.1.2.4
first-order controllable systems
15.5.2.2 | 15.5.2.2 to 15.5.2.2
first-order theory of the reals
The
fixed point of a vector field
Equilibrium
fixed-path coordination
Fixed-path to Fixed-path
flashlight example
2.4.1 to 2.4.1
Boolean expression for
2.5.3
planning graph of
Mutex
flashlight sensor
12.4.3
flat cylinder
2D
flat outputs
Differentially | Differentially
flat torus
2D
flexible materials
Flexible
flying an airplane
13.1.3.2 to 13.1.3.2
folding problems
7.5 to Protein
foliation
14.2.1.1 | 15.4.2.1
force
Newton's | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.2 | 13.3.3 | 13.3.3 | 13.3.3 | The | The | A | 13.4.1.2 | 13.4.1.2
resultant
13.3.2.1 | 13.3.2.2
force sensor
Boundary
formal Lie algebra
Formal to The
forward projection
10.1.2 | 14.2.1.2
differential
13.5.1 to 13.5.1
nondeterministic
Nondeterministic to Nondeterministic
probabilistic
Probabilistic to Probabilistic
under a fixed plan
Forward to Forward
forward search
2.2.1 to Iterative
A algorithm
A-star
A algorithm
to A-star
best first
Best to Best
depth-first
Depth to Depth
Dijkstra's algorithm
Dijkstra's to Dijkstra's
general, discrete
2.2.1 to 2.2.1
iterative deepening
Iterative to Iterative
forward value iteration
2.3.1.2 to 2.3.1.2
four-bar mechanism
Three
frame axiom
2.5.3
Fraunhofer Chalmers Centre
Sealing | Sealing
Frazzoli
14.2.3 | 14.2.3
free space
Obstacle
free variables
Tarski
frequentist
9.5.2.1 to 9.5.2.1
frequentist risk
9.5.2.1
friction cone
Compliant
Frobenius theorem
15.4.2.4 to 15.4.2.4
frontier set
8.5.2.3 | Nondeterministic | 14.5.2
fully actuated system
Underactuation
function space
Vector | 11.4.1 | 13.4.1.1
functional
13.4.1.1 | 13.4.1.1
shortest-path
13.4.1.1
fundamental group
The | The to The
higher order
The
of a simply connected space
The to The
of
The
of
to The
of
The
of
to The
of
The
of
to The
Fundamental Lemma of the Calculus of Variations
13.4.1.1
Gabriely and Rimon
Spanning
gain constant
8.4.4.1
game
alternating-play model
10.5.1 | 11.7.1
extensive form
10.5.1
11.7.1
normal form
10.5.1
open-loop model
10.5.1 | 11.7.1
stage-by-stage model
10.5.1 | 11.7.1
unusual information model
11.7.1 to 11.7.1
game against nature
9.2 to 9.2.4.2
sequential
10.1 to The | 10.6.1 to 10.6.2
game graph
10.5.2
game theory
9. | 9. | 9.3 | 9.3 to 9.4.2 | 9.5.4 to 9.5.4 | 10.5 to Introducing | 11.7 to 11.7.2
information spaces in
11.7 to 11.7.2
game tree
10.5.1 | 10.5.1 | 10.5.1 to 10.5.1.3
information space over
11.7.1 to 11.7.1
12.3.4 to I-space
gap sensor
Depth-mapping
gap theorems
Real
garage configuration
Gaussian sampling
Gaussian to Gaussian
Geiger counter sensor
Landmark
general linear group
Matrix
general position
General | General to General | Critical
generalized coordinates
13.4.1.2
generalized cylinder
Generalized
generalized damper model
Compliant
generalized forces
13.4.1.3 | 13.4.1.4 | 13.4.1.4 | 13.4.3.1
generalized momentum
13.4.4
generator of a lattice
Making
geodesics
13.4.1.2 | Riemannian
geometric modeling
3.1 to Generalized
Gilbert-Johnson-Keerthi algorithm
Further
3.1.2 | Semi-algebraic | The
globally asymptotically stable
Domains
globally positive definite
Determining
globally randomized plan
11.7.1
goal recognizability
11.3.1 to 11.3.1 | Backprojections
goal sensor
12.3.3
Goldberg and Mason
Squeezing
golden ratio
Low-discrepancy
Goursat normal form
15.5.2.3
Grübler's formula
4.4.3
graph search
on an information space
Graph-search to The
grasped configurations
Stable
gray-scale map
Bitmaps | 12.3.2
great circle
5.1.2
grid
7.1.3
2D planning on
2.1.2
feedback plan on
Feasibility to Feasibility
infinite sequence
Infinite to Infinite
multi-resolution
Infinite
neighborhoods
Neighborhoods to Neighborhoods
partial
Infinite
resolution issues
Grid to Grid
set of environments
12.3.1 to Algorithms
grid point
Discretization
grid resolution
5.2.3
group
Groups
group axioms
Groups | Groups to Groups
guaranteed reachable
Convergence
5.6.2
gyroscope
Simple
Haar measure
5.1.4 | 5.1.4 to 5.1.4
hairy ball theorem
8.4.1.1
half-edge
Polyhedral | 6.2.1
half-plane
Convex
half-space
Polyhedral
Halton sequence
Low-discrepancy | Low-discrepancy to Low-discrepancy
Hamilton's equations
13.4.4 | 15.2.3 | 15.4.1 | Pontryagin's | Pontryagin's
Hamilton's principle of least action
13.4.1.2 | 13.4.1.2 to 13.4.1.2
Hamilton-Jacobi-Bellman equation
10.2.2 | 15.2.1 | 15.2.1.2 to 15.2.1.3
Hamilton-Jacobi-Isaacs equation
15.2.1.3
Hamiltonian function
13.4.4 | 13.4.4 | 15.2.3 | 15.2.3
Hammersley point set
Low-discrepancy | Low-discrepancy to Low-discrepancy
harmonic potential function
8.4.4.4 to 8.4.4.4
Hausdorff axiom
Some
Hausdorff space
Some
helicopter flight
14.2.3
Hessian
8.4.4.3
hide and seek
Playing
hierarchical inclusion of a plan
Hierarchical | 12.5.1
hierarchical planning
Hierarchical | Manipulation
higher order controllability
15.5.2.3
Hilbert space
Vector
hill function
Determining
history
History | History to History
history information space
11.1.2 | The to The | 11.4.2 to 11.4.2
at stage
The
at time
11.4.2
history information state
History | History to History
history-based sensor mapping
11.4.1 | 11.4.2
hitch length
13.1.2.4
holonomic
13.1.3.4 | 15.4.2.1
homeomorphic spaces
Homeomorphism:
homeomorphism
Homeomorphism: | Homeomorphism: to Homeomorphism: | Smoothness | Coordinates
homicidal chauffeur
13.5.2 | 13.5.2 to 13.5.2
homing sensor
Landmark
homogeneous transformation matrix
Combining | Combining | no title | Homogeneous | Homogeneous | 3.3.2 | Two | no title | The | The | The | The | The | Linear | Exercises | 4.2.1 | 4.3.3 | 4.3.3 | Chains
homology
The
homotopic paths
Simply
humanoid
Virtual | Virtual | Virtual | Virtual | Virtual | Junctions
hybrid state space
7.3.1 | 7.3.1
hybrid system
7.3 | 7.3.1 to 7.3.1 | Piecewise-smooth | Piecewise-smooth
motion planning
7.3.1
with nature
10.6.2 to 10.6.2
ibuprofen
Designing | Designing
ideal distance function
The to The
identification of points
Identifications
identity sensor
11.1.1 | Linear
implicit function theorem
13.1.1.3
implicit velocity constraints
General
improper prior
9.5.2.2
incomparable actions
9.1.1.2
incremental distance computation
5.3.3
incremental sampling and searching
5.4.2 to Grid
general framework
5.4.1 to 5.4.1
under differential constraints
14.4 to Sampling-based
independent events
Conditional
independent-joint motion model
General to General
inertia matrix
Inertia | Inertia | Inertia to Simplifying | 13.4.1.2
inertial coordinate frame
Inertial | Inertial to Inertial | Newton's | Important | The | The | The | The | 13.4 | 13.4.1.2
infimum
9.1.1.1
infinite reflection (in a game)
9.5.4
infinite-horizon problem
10.3 | 10.3 to Solutions
inflection ray
Critical
information mapping
11.2.1 | 11.2.1 to Constructing
sufficient
Constructing to Constructing
information space
11. to 11.7.2
continuous examples
11.5 to 11.5.4
continuous time
11.4.2 to 11.4.2
conversion to a state space
The to The | 12.1.1 to 12.1.1
discrete examples
11.3 to 11.3.3
for game theory
11.7 to 11.7.2
in continuous state spaces
11.4 to 11.5.4
limited memory
11.2.4 to Sensor
sensor feedback
Sensor to Sensor
information state
11. | 11.
information transition equation
The to The
derived
Constructing to Constructing
information transition function
The
information-conservative property
12.4.2
information-feedback plan
11.1.3
initial condition space
The | The to The | 11.4.1 to 11.4.1
input string
11.3.2
integrable system
13.1.3.4
integral curve
An | An to An
integral manifold
15.4.2.1
interior of a set
Special
interior point
Special
interpolation neighbors
8.5.2.1
interpolation region (for value iteration)
8.5.2.1 | 10.6.1
interval homeomorphisms
Homeomorphism: to Homeomorphism:
intractable problem
Languages
inverse Ackerman function
6.5.2
inverse control problem
Reverse-time
inverse kinematics problem
What
involutive distribution
15.4.2.4
Isaacs
13.5.2 | 13.5.2
isomorphic graphs
Homeomorphism:
isomorphic groups
Using
isomorphism
Homeomorphism:
iterative deepening
Iterative | Iterative to Iterative
Jacobi identity
15.4.3.1 | 15.4.3.3 | 15.4.3.3 | 15.5.2.2
Jacobian
6.4.3
jerk (third time derivative)
13.2.1.3 | 14.6.3.4
joint encoder
Simple
Junctions
Kagami
Virtual | Virtual
Kalman filter
11.6.1 to 11.6.1
Kalman rank condition
Classical
Kd-tree
Approximate | Approximate to Approximate | Defining | Maintaining
Khalil-Kleinfinger parameterization
Junctions
Khatib
8.4.1.2
kidnapped-robot problem
12.2
kinematic chain
3.3
kinematic constraints
Nonholonomic | 15.4.1
kinematic singularities
Computing
kinematically controllable
Decoupling
kinematics for wheeled systems
13.1.2 to 13.1.2.4
Kineo CAM
An | An | An | Parking | Parking
kinetic energy
13.3.2.1 | 13.3.2.1 | Completing | 13.4.1.2 | 13.4.1.2 | 13.4.1.2 | 13.4.1.2 | 13.4.1.4 | 13.4.1.4 | 13.4.2 | 13.4.2.1 | 13.4.2.1 | 13.4.3.1
kinodynamic planning
Kinodynamic | Kinodynamic to Kinodynamic | 14.4.1 to Underactuated
Klein bottle
2D
knot
Simplifying
knot simplification
Simplifying to Simplifying
knot vector
Nonuniform
Koditschek
Kolmogorov complexity
2.4.1 | Lower
Kuffner
A | A | 5.4.1
Kuhn
11.7.1 | Further
Kutzbach criterion
4.4.3
L-shaped corridor example
11.3.1 to 11.3.1
label-correcting algorithms
2.3.3 | 2.3.3 to 2.3.3
Lafferriere and Sussmann
15.5.1
Lagrange multiplier
13.4.3.1
Lagrangian function
13.4.1.2 | 13.4.1.4 | 13.4.2.1 | 13.4.3.1 | 13.4.4
Lagrangian mechanics
4.
landmark region detector
Landmark
landmark sensors
Landmark to Landmark
language
2.1.2 | 11.3.2
latitude in a grid
Algorithms
Latombe
4.
lattice
Making | Making | Making | Low-discrepancy
for unconstrained mechanical systems
Unconstrained to Unconstrained
Laumond
Parking | Parking | Nonholonomic
lawn mowing
7.6
layered graph
Planning
layered plan
Plan
learning phase
The
leaves of a foliation
14.2.1.1 | 15.4.2.1
Lebesgue integral
5.1.3
Lebesgue measure
5.1.3 | 5.1.3 to 5.1.3
left translation
15.4.3.1
left-invariant vector field
15.4.3.1
left-turn predicate
6.2.4
Legendre transformation
13.4.4
Legendre-Clebsch condition
15.2.3
Leibniz rule
Tangent
Drug
Lens spaces
Higher
level-set method
Further
Lie
15.4.2
Lie algebra
15.4.3.1 | 15.4.3.1 | 15.4.3.1 to 15.4.3.2
cross product example
15.4.3.1 to 15.4.3.1
of the system distribution
15.4.3.2 to 15.4.3.2
on Lie groups
15.4.3.1 to 15.4.3.1
Lie algebra rank condition
15.4.3.4
Lie bracket
15.4.2.3 | 15.4.2.3 to 15.4.2.3 | 15.4.3.1
Taylor series approximation of
15.4.2.3 to 15.4.2.3
Lie derivative
Determining
Lie group
Matrix | 15.4.3.1 | 15.4.3.1
ligand
Drug
limit curve
14.6.3.5
limit cycle
Limit | Limit to Limit
limit point of a set
Special
Lin-Canny
Further
line-segment robot
6.3.4 to Complexity
linear combination
Vector
linear complementarity problem
9.4.1.2
linear differential game
13.5.2 | 13.5.2
linear interpolation
8.5.2.1
linear momentum
13.3.2.1
linear programming
9.1.1.1 | 9.3.3.2 | 14.7
linear sensing models
Linear | Linear to Linear
linear space
Vector
linear system
13.2.2 | 13.2.2 to 13.2.2
observability
13.2.2
time-varying
13.2.2
linear transformations
Linear
linear-Gaussian system
11.6.1 | 11.6.1 | Continuous
15.2.2 to 15.2.2
11.6.1 | 15.2.2
3.3
3.3
4.4.3
Lipschitz condition
5.3.4 | An | An to An | 14.2.2.3 | 14.2.2.3 | 14.2.2.3 | 14.3.4 | Resolution | 14.5.2 | 14.7
Lipschitz constant
5.3.4 | An
LMT framework
12.5.1
local operator
Navigation | Navigation | Navigation to Navigation | Computing | 8.2.3 | 8.4.1.2 to 8.4.1.2 | 8.4.2 | 8.4.4.3 | Using | 14.5.2
continuous space
8.4.1.2
local planning method
5.4.1 | 5.4.1 | 5.4.1 | 5.4.1 | 5.4.1 | 5.4.3 | 5.4.3 | Ariadne's | Expansive-space | Random-walk | 5.5.1 | Generic | Some | Some | Some | Planning | 14.7 | 15. | STLC: | 15.3 | 15.3.1 | 15.3.1 | 15.3.2 | 15.3.3 | 15.4.3.4 | 15.5 | Decoupling
in plan-and-transform
14.6.2 | 14.6.2 | 14.6.2 | 14.6.2 | 14.6.2 | 14.6.2 | 14.6.2 | 14.6.2
under differential constraints
14.3.3 | 14.3.3 to 14.3.3 | 14.3.4 | 14.4.3 | 14.4.3 | 14.4.3 | Designing | Designing
local visibility sensor
12.3.3
localization
12.2 to The
active
12.2 | Solving to Solving
combinatorial
12.2.2 to 12.2.2
discrete
12.2.1 to Other
passive
12.2 | 12.2.1 | Solving to Solving
probabilistic
12.2.3 to Continuous
symmetries
Solving to Solving
locally positive definite
Determining
locally randomized plan
11.7.1
Logabex LX4 robot
Computing | Computing
logic-based planning
2.4 to 2.5.3
as satisfiability
2.5.3 to 2.5.3
converting to state space
2.4.2 to 2.4.2
in plan space
2.5.1 to 2.5.1
operator
2.4.1
via a planning graph
2.5.2 to Plan
loop path
The
lost-cow problem
Competitive | Exercises | Exercises
low-discrepancy sampling
5.2.4 to Low-discrepancy
low-dispersion sampling
5.2.3 to Dispersion
lower envelope
6.5.2 | 6.5.2 | 6.5.2 | 6.5.2 | 6.5.2 | 9.3.3.2
lower pairs
3.3.2 | 3.3.2
lower value of a game
Lozano-Pérez
4.
Lozano-Pérez, Mason, and Taylor
12.5.1
lunar lander
13.3.2.1 to 13.3.2.1
Lyapunov function
8.5.1 | 15.1.2 | Determining to Lyapunov
in planning
Lyapunov to Lyapunov
Lyapunov stability
Equilibrium | Equilibrium to Equilibrium
uniform
Equilibrium
Lynch and Mason
13.1.3.1 | 13.1.3.1
Möbius band
2D | 2D | The | Exercises | Exercises
Mahalanobis metric
The
maneuver
14.2.3
maneuver automaton
14.2.3 | 14.2.3 | 14.2.3
Manhattan metric
Manhattan motion model
General to General
manifold
4.1.2 | Manifold | Manifold to Higher
embedding
Manifold
higher dimensional
Higher to Higher
one-dimensional
1D to 1D
two-dimensional
2D to 2D
with boundary
Manifold
manipulation graph
Manipulation to Manipulation
manipulation planning
7.3.2 to Multiple
under uncertainty
12.5 to Squeezing
manipulator
The | What | Further | 7.3.2 to Multiple | 7.4 | 7.4 | 7.4 | Carton | 13.4.2.1 to 13.4.2.1 | 14.6.3 | 14.6.3.3
map building
12.3 | 12.3.1 to The
marginalization
Marginalization | Marginalization to Random | Probabilistic | Probabilistic | 11.2.3 | 11.2.3 | Discrete
Markov chain
10.1.1
Markov decision process
10.1.1
Markov game
Introducing
Markov process
10.1.1 | 10.1.1
mass matrix
13.4.1.2
matching pennies
9.1.3 to 9.1.3
Matlab
14.7
matrix game
9.3.1
matrix groups
Matrix to Special
matrix subgroup
Matrix
maximal ball
Medial-axis
Maximum to Maximum
6.2.3 | 6.2.3 to 6.2.3
maze searching
Algorithms to Algorithms
Mealy/Moore machines
2.1.2
means-end analysis
Further
measurable function
5.1.3
measurable sets
5.1.3
measure axioms
5.1.3
measure space
5.1
measure theory
5.1.3 to 5.1.4 | Defining to Defining
measure zero
5.1.3
mechanics
13.3 to 13.4.4
Hamiltonian
13.4.4 to 13.4.4
Lagrangian
13.4.1 to 13.4.3.2
Newton-Euler
13.3 to A
medial-axis sampling
Medial-axis to Medial-axis
Mersenne twister
Pseudorandom
metric space
5.1 | 5.1.1 | 5.1.1 to Cartesian
Cartesian products of
Cartesian to Cartesian
definition
5.1.1
for motion planning
5.1.2 to Pseudometrics
from
5.1.2
from
to 5.1.2
from
5.1.2
from
to 5.1.2
from
5.1.2
from
to 5.1.2
from
5.1.2
from
to 5.1.2
from
5.1.2 to 5.1.2
nonpositively curved
14.7
Riemannian manifold
Riemannian to Riemannian
robot displacement metric
5.1.2
subspaces of
Metric to Metric
metric tensor
Riemannian
metrizable
5.1.1
mine sweeping
7.6
minimalism
12.5.2
minimax
9.2.2
13.1.2.1
Minkowski difference
4.3.2 | 6.2.1 | 6.2.1 | Specialized | Specialized
Minkowski sum
4.3.2
mod sensor
11.1.1 to 11.1.1
mode space
7.3 | 7.3.1
mode transition function
7.3.1
mode-dependent dynamics
7.3
moment of a density
Moment-based
moment of force
13.3.2.2
moment of inertia
Simplifying
moment of momentum
13.3.2.1 | 13.3.2.1 | 13.3.2.2 | The | Differential | The
moment-based approximations
Moment-based to Moment-based
momentum
13.3.2.1
monomial
Polynomials
monotone polygon
Triangulation
morphing a path
Simply
Morse function
8.4.4.3
Morse theory
8.4.4.3
motion capture
Further
motion command
Motion | Motion | Motion to Motion
motion planning
14.1.2.2
motion primitive
14.2.3 | 14.2.3 to 14.2.3 | Designing to Designing
multi-chained-form systems
15.5.2.3
multi-level approach
14.6.2
multi-linear interpolation
8.5.2.1
multi-resolution grid
Infinite
multiobjective optimization
9.1.1.2 to 9.1.1.2
multiple observations
Receiving to Receiving
multiple query
Notions | 5.6
multiple shooting
14.7
multiple-robot motion planning
7.2 to Planning
multiple-robot optimality
7.7.2 to Computing
multiply connected
Simply
Murphy's Law
9.2.2
Murray and Sastry
15.5.2
mutex condition
Mutex | Mutex to Mutex
mutex relation
Mutex
NAG Fortran Library
14.7
naive Bayes
Receiving
narrow-phase collision detection
Two-phase
NASA/Lockheed Martin X-33
14.1.3.1 | 14.1.3.1 | 14.1.3.1
Nash equilibrium
9.4 to 9.4 | 9.4.1 | 9.4.1 to Summary | 9.4.2 | 9.5.4 to 9.5.4 | 11.7 | 11.7.2
9.4.1.1
in a sequential game
Nash to Nash
nonuniqueness
9.4.1.1 to 9.4.1.1
randomized
9.4.1.2 | 9.4.1.2 to 9.4.1.2 | 9.4.2 | 11.7.1
nature
9. | 9.2.1
nature action space
9.2.1
nature observation action
Nature | Nature
nature sensing action
11.1.1 | 11.1.1 | 11.4.1 | Linear to Linear | 11.5.3 to 11.5.3
nature sensing actions
11.1.1
2.2.1 | 2.3.2 | Navigation to Other
continuous space
8.4.1.2
in the sense of Rimon-Koditschek
8.4.4.3 to 8.4.4.3
stochastic
10.6.2 | 10.6.2
12.3.1 | Algorithms to Algorithms
negative literal
2.4.1 | 2.4.1
neighborhood function
5.6.2
neighborhood of a cover
8.5.1.1
Neumann boundary condition
8.4.4.4
Newton's laws
Newton's to Newton's | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.1 | 13.3.2.2 | The | The | Inertia | 13.4.1.2 | 13.4.1.2
next-best-view problem
12.3.5
Maximum
nicotine
Designing | Designing
nilpotent
15.5.1
nilpotent system
15.4.3.3
nilpotentizable
15.5.1
Nilsson
Further
Nixederreiter-Xing sequence
Low-discrepancy
nonconservative forces
13.4.3.2 to 13.4.3.2
nonconvex
polygon
Nonconvex to Nonconvex | Nonconvex
polyhedron
Nonconvex
set
Convex
noncooperative game
9.
noncritical regions
Critical
nondeterministic finite automaton
11.3.2 | 11.3.2 to 11.3.2
nondeterministic information space
11.2.2 to 11.2.2
approximations
Conservative to Conservative
examples
11.3.1 to 11.3.2
planning on
12.1.2 to The
nondeterministic Turing machine
Languages
nondeterministic uncertainty
9.2.2 to 9.2.2
criticisms of
9.5.3 to 9.5.3
nondirectional backprojections
Backprojections
nonholonomic
13.1.3.4 | Nonholonomic | Nonholonomic | 15.4 | 15.4.2.1
nonholonomic constraints
13.1.2
nonholonomic integrator
13.2.3 | 13.2.3 to 13.2.3 | 15.4.2.3 | 15.4.3.4 | 15.5.1 | The | The
showing it is nonholonomic
15.4.2.4 to 15.4.2.4
steering
15.5.2.1 to 15.5.2.1
nonholonomic metric
The
nonholonomic planning
Parking | Nonholonomic | Nonholonomic to Nonholonomic
nonholonomic system
Underactuated to Underactuated
nonholonomic system theory
15.4 to 15.4.3.5
noninformative prior
9.5.2.2 | 9.5.2.2 to 9.5.2.2
nonintegrable
15.4.2.1
nonlinear optimization
14.7
nonlinear programming
14.7 | 14.7 to 14.7
nonlinear system
13.2.3 to 13.2.3
affine in control
15.4.1 to 15.4.1
affine-in-control
13.2.3
nonparametric methods
9.5.2.3
nonpositively curved space
14.7
nonprehensile manipulation
12.5.2 | 12.5.2 to Squeezing
nonrigid transformations
3.5
nonzero-sum game
9.4 to 9.4.2
with more than two players
9.4.2 to 9.4.2
with two players
9.4.1 to Summary
NP (complexity class)
Languages
null sensor
11.1.1 | 11.1.1 to 11.1.1
numerical continuation
Stepping
numerical integration
Euler
Euler to Euler
multistep methods
Multistep | Multistep to Multistep
Runge-Kutta
Obtaining | Runge-Kutta to Runge-Kutta
single-step methods
Multistep
NURBS
Nonuniform to Nonuniform
OBB
5.3.2 | 5.3.2
observability
13.2.2
observation space
Formulating | 11.1.1 | 11.1.1
observations
9.2.3 to Receiving
obstacle region
3.2 | Obstacle
in the C-space
4.3 to Chains
1D case
to 4.3.2 | 4.3.2
general case
4.3.3 to Chains
polygonal case
A to Computing
polyhedral case
A to A
in the state space
14.1.3 to 14.1.3.2
in the world
3.1 | 3.1 to Generalized
polygonal case
6.2 to 6.2.4
time-varying
7.1.1 | 7.1.3 to 7.1.3
obstacles
3.1
occupancy grid
Bitmaps | The
Ochiai unknot benchmark
Simplifying | Simplifying
octane transformations
The to The
odd/even sensor
11.1.1 to 11.1.1
odometric coordinates
Solving | Solving | Algorithms
odometry sensors
Odometry to Odometry
on-line algorithm
1.4.1 | Competitive to Competitive
open ball
Some
open set
3.1.2 | 4.1.1
open-loop
control law
Open-loop
plan
8.1
operator
2.4.1
optical character recognition
A to A
optimal motion planning
7.7 to Computing
optimal planning
discrete
2.3 | 2.3.2 to 2.3.3
fixed-length plans
2.3.1 to 2.3.1.2
unspecified length
2.3.2 to 2.3.2
optimization
9.1.1 | 9.1.1.1 to 9.1.1.2
orientation sensor
Simple
oriented bounding box
5.3.2 | 5.3.2
orienteering problem
Further
origami
7.5
orthogonal group
Matrix
General
painting
7.6
parallel manipulator
7.4
parallel-jaw gripper
Squeezing
parameter estimation
9.2.4.2 | 9.2.4.2 to 9.2.4.2
parameterization
1D | Coordinates
Pareto optimal
7.7.2 | 7.7.2 | 7.7.2 to Computing | 9.1.1.2 to 9.1.1.2 | 9.4.1.1 | 9.4.1.1 | 9.4.2 | 9.5.2.1
parking a car
Parking | 13.1.2.1 | 13.2.4.3 | 14.2.1.2 | Exercises | Classical | 15.4.2.3
part configuration space
partial grid
Infinite
partial plan
2.5.1
particle
13.3.2
dynamics
13.3.2 to 13.3.2.1
falling
13.4.1.2 to 13.4.1.2
on a sphere
13.4.3.1 to 13.4.3.1
particle filtering
Particle | Particle to Particle | Continuous
path
Paths | Paths to Paths
path connected
Connected
path tuning
7.1.3 | 7.1.3
path-constrained phase space
14.6.3.3
path-directed subdivision tree
Other
pattern classification
9.2.4.1 | 9.2.4.1 to A
pebble
Landmark
peg-in-hole problem
12.5.1 | Backprojections | Backprojections | Backprojections | Backprojections | Backprojections
pendulum
13.3.2.1 to 13.3.2.1
double
Exercises
Pennsylvania Turnpike
9.1.1.2
perfect recall
11.7.1
permissible action trajectories
14.1.1
Pfaffian constraints
13.1.1.3 to 13.1.1.3 | 13.1.1.3 | 13.1.2.1 | 13.1.3.4 | 13.2.3 | 13.2.3 | 13.4.3.1 | 13.4.3.1 | 13.4.3.1 | 13.4.3.1 | 15.4.1 | 15.4.1 | 15.4.1 | 15.4.1 | 15.4.1 | 15.4.1 | 15.4.1 | 15.4.2.1 | 15.4.2.1 | 15.4.2.1 | 15.4.2.1 | 15.4.2.2 | 15.4.2.2 | 15.4.2.4 | 15.5.2.3
pharmacophore
Drug
phase constraints
14.1.3.1
phase space
13.2 | 13.2.1.1 to 13.2.4.3
obstacles
14.1.3 to 14.1.3.2
path-constrained
14.6.3.3 to 14.6.3.3
phase transition equation
13.2.1.1 | 13.2.1.2
phase vector
13.2.1.1
Philip Hall basis
15.4.3.3 | 15.4.3.3 | 15.4.3.3 to 15.4.3.3 | 15.5.1 to 15.5.1 | Formal | The | Returning | 15.5.2
Piano Mover's Problem
Definition to Definition | 14.1.1 | 14.1.1 | 14.3.3 | 14.3.4 | 14.4.3 | 14.4.3 | Handling | Distance | 14.5.1 | 14.6.1 | 14.7
piecewise-linear obstacle motion
7.1.1 to 7.1.1 | 7.1.3
pitch rotation
Yaw, | Yaw,
plan-and-transform method
14.6.2 to 14.6.2
plan-based state transition graph
Graph
plan-space planning
2.5.1 | 2.5.1
plane-sweep principle
Plane-sweep to Algorithm
6.2.4 | 8.4.3
General
planner
1.4.2 to 1.4.2
planning graph
2.5.2 | Planning to Plan
planning under sensing uncertainty
12. to Squeezing
general methods
12.1 to The
manipulation
12.5 to Squeezing
Poinsot
13.3.3 | Completing
point robot
6.2.1
point-location problem
8.5.2.1 | Maintaining
policy iteration
10.2.2 | 10.2.2 to 10.2.2
for reinforcement learning
Policy to Policy
on an information space
Policy to Policy
with average cost-per-stage
Solutions to Solutions
with discounted cost
Policy to Policy
polygonal model
3.1.1 to Nonconvex | 6.2 to 6.2.4
face
6.2.1
half-edge
6.2.1
representation
6.2.1 to 6.2.1
polyhedral model
Polyhedral to Polyhedral
polynomial
Polynomials | Polynomials to Polynomials
coefficient
Polynomials
in formal Lie algebra
Formal
term
Polynomials
total degree
Polynomials
polynomial-time reducible
Hardness
POMDP
11.3.3 | 11.3.3 to 11.3.3 | 12.1.3 to The
Pontryagin's minimum principle
10.2.2 | 14.7 | 15.2.3 | 15.2.3 to Time | Pontryagin's | Pontryagin's to Pontryagin's
time-optimality case
Time to Time
portiernia
7.3.1 | 7.3.1 to 7.3.1
position sensor
Simple
positive definite function
Determining
positive literal
2.4.1 | 2.4.1
posterior
Conditional
potential energy
13.4.1.2 | 13.4.1.2 | 13.4.1.2 | 13.4.1.2 | 13.4.1.4 | 13.4.2 | 13.4.2.1 | 13.4.2.1
potential function
Pseudometrics | 5.4.3 | 13.4.1.2
attractive term
5.4.3
continuous state space
8.4.1.2
discrete
repulsive term
5.4.3
PQP (Proximity Query Package)
Further
predicate
2.4.1
for geometric models
Defining to Defining
preimage of a function
Continuous
preimage of a motion command
Preimages to Preimages
preimage of an observation
11.1.1
preimage planning
12.5.1 | 12.5.1 to Computing
Princess and the Monster
11.7.2 to 11.7.2
principle of virtual work
13.4.3.1
principle subresultant coefficients
The
prior distribution
Conditional | 9.5.2.2 to 9.5.2.2
prioritized planning
Prioritized to Prioritized
prismatic joint
Attaching | Attaching | Attaching | Homogeneous | The
Prisoner's Dilemma
9.4.1.1 to 9.4.1.1 | 9.5.4 | 9.5.4
probabilistic completeness
Notions
probabilistic information space
11.2.3 to Sensor
approximations
Moment-based to Moment-based
examples
11.3.3 to 11.3.3
planning on
12.1.3 to The
probabilistic information state
computation of
11.6 to Particle
probabilistic uncertainty
9.2.2 to 9.2.2
criticisms of
9.5.2 to 9.5.2.3
probability function
Probability
probability measure
5.1.3 to 5.1.3
probability space
Probability to Probability
probability theory
9.1.2 to Expectation
problem solving
2. | 2.
product of inertia
Simplifying
projection sensors
Simple to Simple | 11.5.2 to 11.5.2
projective geometry
Combining
projective space
Higher
protein cavity
8.1
protein folding
Designing | Protein to Protein
proximity sensor
Boundary
pseudometric
Pseudometrics to Pseudometrics | Sampling-based | Sampling-based
pseudorandom number generation
Pseudorandom to Pseudorandom
linear congruential
Pseudorandom
PSPACE
Languages
Puma 560 robot
The
pure strategy
9.1.3
pursuit-evasion game
11.7.2 | 13.5.2 | 13.5.2
pushing a box
13.1.3.1 to 13.1.3.1
Q-factor
10.4.3
Q-learning
10.4.3 to Policy
15.2.2
8.4.1.2 to 8.4.1.2
quantified variables
Tarski
quantifier
Tarski
quantifier-elimination problem
Tarski | The
quantifier-free formula
Tarski
quasi-static
13.1.3
quaternion
Quaternions to Finding
from a rotation matrix
Finding to Finding
quotient topology
Identifications
6.2.4 | 8.4.3
random loop generator
7.4.2 | Loop | Loop to Loop
random sampling
5.2.2 to Testing
of
Generating
of
to Generating
of directions
Generating to Generating
tests
Testing to Testing
random variable
Random | Random to Expectation
random-walk planner
Random-walk to Random-walk
randomized algorithm
General
randomized lower value
9.3.3.1 | 10.5.1.2 | Value
randomized plan
10.5.1 | 10.5.1 | Defining
randomized potential field
5.4.3 | 5.4.3 to 5.4.3 | 8.4.1.2
under differential constraints
Randomized to Randomized
9.3.3.1
randomized security plan
10.5.1.2
randomized strategy
9.1.3 | 9.1.3 to 9.1.3
randomized upper value
9.3.3.1
randomized value
9.3.3.1 | 10.5.1.2
range scanner
Depth-mapping
range space (for discrepancy)
5.2.4
rapidly exploring dense tree
5.5 | 5.5 | 5.5 to More | Sampling-based | Fixed-path | Sampling-based | Computing | Computing
exploration
5.5 to 5.5.1
finding nearest points
5.5.2 to Approximate
making planners
5.5.3 to More
under differential constraints
14.4.3 to Designing
Rapoport
9.5.4
rational decision maker
9.3.1 | 9.5.1.1 | 9.5.1.2
reachability graph
14.2.2.1 | 14.2.2.1 to 14.2.2.1
reachability tree
14.2.2.1 | 14.2.2.1 to 14.2.2.1
reachable set
14.2.1 | 14.2.1.1 to 14.2.1.3
backward
Domains
for simple car models
14.2.1.2 to 14.2.1.2
real algebraic numbers
Real | Real to Real
reality television
9.5.1.1 to 9.5.1.1
reckless driving
Wreckless''
recognizability
11.3.1 | Backprojections
reconfigurable robot
7.3.1
recontamination
12.4.2
Reeds-Shepp car
13.1.2.1 | Symmetric | 14.2.1.2 | 14.2.1.2 | 14.2.1.2 | 14.6.2
Reeds-Shepp curves
15.3.2 | 15.3.2 to 15.3.2
refinement of a plan
Refinement | 14.6.1
reflex vertex
6.2.4
region of inevitable collision
14.1.3.2 | 14.1.3.2 | 14.1.3.2 to 14.1.3.2
regret
Regret | Regret to Regret | Regret to Regret
regret matrix
Regret | Regret
reinforcement learning
10.4 | 10.4.1 | Terminology to Policy
evaluating a plan
10.4.2 to Temporal
general framework
The to The
terminology
Terminology to Terminology
relative value iteration
Solutions
repulsive vertex
8.4.2
reroute path
Solving
resolution
5.2.3
resolution completeness
Notions | 5.2.3 | 5.2.3 | Grid | Fixed-roadmap | 14.2.2.2 | Resolution to Resolution | Ensuring to Ensuring
under differential constraints
14.2.2.2 to 14.2.2.3
resultant
force
13.3.3
moment
13.3.3
reverse-time system simulation
Reverse-time to Reverse-time
revolute joint
Attaching | Attaching | Attaching | Attaching | Attaching | Homogeneous | Homogeneous | 3.3.2 | 3.3.2 | 3.3.2 | 3.3.2 | The | The | The | The | 8.1 | Common | Flexible | Exercises
reward
Terminology
reward function
9.1.1.1
reward functional
Terminology
reward space
9.5.1.1
Riemannian manifold
13.4.1.2
Riemannian metric
Riemannian | Riemannian to Riemannian
Riemannian tensor
Riemannian
Rimon
risk
conditional Bayes'
Optimal
frequentist
9.5.2.1
directed
Sampling-based
general requirements
robot displacement metric
5.1.2 to 5.1.2
robot-robot collisions
7.2.1
Rock-Paper-Scissors
9.5.4 | Exercises
roll rotation
Yaw, | Yaw,
rolling a ball
13.1.3.3 to 13.1.3.3
rotation
2D
Rotation to Combining
3D with quaternions
Quaternions to Quaternions
3D with yaw-pitch-roll
Yaw, to The
Rubik's cube
1.1 | Discrete | Discrete | Time | 2.1.2
Runge-Kutta
Obtaining
Russell and Norvig
2.
sample point of a cell
Defining
sample sequence
5.2.1
sample set
5.2.1
sample space (of a probability space)
Probability
sampling-based neighborhood graph
8.5.1.3
sampling-based planning
for closed chains
Sampling-based to Computing
philosophy
5. | 5. | 5. to 5.
time-varying
Sampling-based to Sampling-based
under differential constraints
14.3 to Sampling-based
with feedback
8.5 to 8.5.2.3 | 14.5 to 14.5.2
-goodness
Some
analysis
Some to Some
basic method
5.6 to Some
boundary sampling
Sampling to Sampling
bridge-test sampling
Bridge-test to Bridge-test
Guassian sampling
Gaussian to Gaussian
medial-axis sampling
Medial-axis to Medial-axis
preprocessing phase
Generic to Selecting
query phase
Query to Query
vertex enhancement
Vertex to Vertex
5.6.2 to 5.6.2
5.6 | 5.6 to Medial-axis
under differential constraints
Sampling-based to Sampling-based
Sard's Theorem
8.4.4.3
scalarization
Scalarization to Scalarization
scaling an object
Linear
screw transformation
Two
sealing cracks
Sealing
search algorithms
7.1.3
5.4.2 to Grid
under differential constraints
14.3.4 to 14.3.4 | Searching to Searching
unified view
2.2.4 to 2.2.4
search graph
2.2.4 | 5.4.1 | 14.3.4
searching an environment
12.3.1
second-order controllable systems
15.5.2.2
second-order differential drive
13.2.4.3
second-order unicycle
13.2.4.1
section (of a cylinder)
The
sector (of a cylinder)
The
security plan
10.5.1.1 | 10.5.1.1 to 10.5.1.1 | Value
security strategy
9.3.2
randomized
9.3.3.1
selective sensor
11.1.1 to 11.1.1
semi-algebraic decomposition
Semi-algebraic
semi-algebraic model
3.1.2 to 3.1.2
semi-algebraic set
3.1.2
sensing history
History
sensor feedback
Sensor
sensor mapping
11.1.1 | 11.1.1 | 11.1.1 to 11.1.1 | 11.4.2 to 11.4.2
sensor observation
11.
sensorless manipulation
12.5.2
sensorless planning
11.3.1 | 11.3.1 to 11.3.1 | 11.5.4 to 11.5.4
sensors
continuous
11.5.1 to Odometry
discrete
11.1.1 to 11.1.1
sequential game
10.5 | 10.5.1 to Introducing
information space of
11.7.1 to 11.7.2
Markov assumption
10.1.1 to 10.1.1
more than two players
Introducing to Introducing
on state spaces
10.5.2 to Introducing
10.5.1.2 to 10.5.1.3 | Saddle to Saddle | 11.7 | 11.7.1 to 11.7.1 | 11.7.1 to 11.7.1 | 11.7.2 to 11.7.2
zero-sum with nature
Introducing to Introducing
12.3.4
12.3.4
shearing transformation
Linear
shooting methods
14.7
shortest-path functional
13.4.1.1 to 13.4.1.1
6.2.4 | 6.2.4 | 6.2.4 to 6.2.4 | Using
SICK LMS-200
Depth-mapping
sigma algebra
5.1.3
sign assignment
Semi-algebraic
sign sensor
11.1.1 to 11.1.1
sign-invariant region
Semi-algebraic
silhouette curves
6.4.3 | 6.4.3
simple polygon
Nonconvex
simple-car model
13.1.2.1 to 13.1.2.1
two-car game
13.5.2 to 13.5.2
with nature
13.5.1 to 13.5.1
simple-unicycle model
13.1.2.3 to 13.1.2.3
simplicial complex
6.3.1 | Simplicial | Simplicial | Simplicial to Simplicial | Singular | Singular to Singular
simply connected space
Simply
9.5.1.2
simulation-based methods
Terminology
simultaneous localization and mapping
12.3.1
single query
Notions | 5.4.1
single shooting
14.7
singular 0-simplex
Singular
singular arcs
15.2.3
singular complex
6.3.1 | Singular | Singular | Singular
singular distribution
15.4.2.2
singular matrix
6.4.3
singular point of a distribution
15.4.2.2
singular simplex
Singular
singular value decomposition (SVD)
10.2.2
situation calculus
2.5.3
skew symmetry
15.4.3.1 | 15.4.3.3
SLAM
12.3 | 12.3.1 to The
probabilistic
12.3.5 to The
sliding-mode control
Piecewise-smooth
sliding-tile puzzle
1.1 | Discrete | Discrete | 2.1.2
small-time local controllability
13.1.2 | 13.1.2.1 | 14.6.2 | STLC: to STLC: | 15.3.1 | 15.3.2 | 15.4 | 15.4.2 | 15.4.3 | 15.4.3.4 to 15.4.3.5 | 15.5 | 15.5.1 | Decoupling
smooth differential drive
13.2.4.3 to 13.2.4.3
smooth distribution
15.4.2.2
smooth function
Smoothness
smooth manifold
Manifold | 8.3.2 | Coordinates to Vector | 15.4.2.2
Coordinates
to Coordinates
Coordinates
to Coordinates
Coordinates
to Coordinates
Riemannian
Riemannian to Riemannian
smooth structure
Coordinates
smoothness of a function
Smoothness to Smoothness
Sobol sequence
Low-discrepancy
Sod's Law
9.2.2
Sokoban
Lower
solid representation
3.1
solution in the sense of Filipov
Piecewise-smooth
solution trajectory
An | Vector
span of vector fields
15.4.2.2
spanning tree
Spanning
spanning-tree covering
Spanning to Spanning
spatial constraints
Drug
special Euclidean group
Special | Special to Special | Special to Special
special orthogonal group
Matrix
speedometer
Simple
spherical coordinates
Tangent
spherical joint
The | 8.1
spherical linear interpolation
5.1.2
spine curve
Generalized
spiral search
Competitive
squeeze function
Squeezing
squeezing parts
Squeezing to Squeezing
stability of a system
15.1.1 to Determining
time-varying case
Time-varying to Time-varying
uniform
Equilibrium
stable configuration space
Stable
stage-dependent plan
Defining
standard grid
Making
star algorithm
A to A
star-shaped regions
8.4.4.3
state estimation
Making to Making
state history
8.4.1.1
state mapping
11.4.1
state space
2.1.1 | 2.1.1
state trajectory
8.2.1 | 8.4.1.1 | 14.1.1
state transition equation
2.1.1 | 2.1.1 | 13.2.1.1 | 13.2.1.2
state transition function
2.1.1 | 2.1.1
state transition graph
2.1.1
state transition matrix
Probabilistic
state-nature mapping
11.4.1 | 11.4.2
state-sensor mapping
11.4.2
state-space discretization
14.4.2 to Backward
stationary cost-to-go function
2.3.2 | Convergence | Convergence | Using
stationary differential equations
Vector
statistical decision theory
9.2.4
steering methods
14.3.3 | 15.5 to Dynamic
piecewise-constant actions
15.5.1 to Using
sinusoidal action trajectories
15.5.2 to 15.5.2.3
steering problem
14.1.2.2
Stentz's algorithm
General | 12.3.2 to Interpretation
stereographic projection
Solving | Coordinates
sticking
Compliant | Backprojections | Backprojections | Backprojections | Computing | Computing
stochastic control theory
10.
stochastic differential equation
13.5.1
stochastic fractal
5.5.1
stochastic iterative algorithm
Temporal | Temporal
stochastic shortest-path problem
Further
strange topology
Some
strategy
Formulating
STRIPS
2. | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.2 to 2.4.2 | 2.5
strong backprojection
Backprojections | Backprojections | Backprojections | Backprojections | Backprojections
structure problem
Protein
sub-Riemannian metric
The
subgroup
Matrix
subjective probabilities
9.5.2.2
subspace topology
Some | Some to Some
sufficient information mapping
Constructing
sufficient statistic
Constructing
Sukharev grid
Making
supremum
Dispersion | 9.1.1.1
Sussmann and Tang
15.3.2 | 15.3.2
swath
5.5.1 | 5.5.1 | 5.5.1 | 14.2.2.1 | 14.2.2.1 | 14.2.2.1 | 14.2.3 | 14.3.4
swath-point selection method
5.5.1 | 14.3.4 | 14.3.4
Swiss cheese
Simply
switching boundary
Piecewise-smooth
switching time
15.2.3
symmetric systems
Symmetric to Symmetric
symmetric Turing machine
Lower
symmetry class
Solving
symplectic manifold
13.4.4
system
13. | Open-loop
determining whether controllable
15.4.3 to 15.4.3.5
determining whether nonholonomic
15.4.2 to 15.4.2.4
distribution
15.4.2.2
simulator
14.3.2 to Reverse-time
system vector fields
15.4.1
systematic search
2.2 to 2.2
tangent bundle
Vector | 13.2.1.2 | 15.4.2.2
tangent point
14.6.3.5
tangent space
Vector | Vector to Vector | 8.3.2 | 8.3.2 | 8.3.2 to 8.3.2 | Tangent
on a manifold
Tangent to Tangent
TangentBug
Using
Tarski sentence
Tarski
Tarski-Seidenberg Theorem
Semi-algebraic
Taylor series
15.2.1.2 | 15.2.1.3 | 15.4.2.3 | 15.4.2.3 | 15.4.2.3 | 15.4.2.3
team theory
11.7.2
temporal difference
Temporal | Temporal to Temporal
temporal logic
Further
termination action
2.3.2 | 11.1.3
THC
Designing | Designing
theory of computation
6.5.1
time scaling
7.1.3 | Trajectory
time-invariant
13.2.2
time-limited reachable set
14.2.1.2
time-monotonic path
7.1.1 | Sampling-based | Combinatorial | Combinatorial | Combinatorial | Combinatorial | 7.1.3 | 7.1.3
time-optimal trajectory planning
14.6.3.5 to 14.6.3.5
time-varying motion planning
7.1 to 7.1.3
algebraic obstacle motion
Combinatorial
bounded speed
Bounded to Bounded
unbounded speed
7.1.1 to Combinatorial
timing function
7.1.3
tire skidding
A
Tit-for-Tat
9.5.4
topological complexity
Further
topological graph
Homeomorphism: | Homeomorphism: to Homeomorphism: | 14.2.2.1
topological property
14.6.2 | 15.4.3.4
topological space
4.1.1 | 4.1.1 to Homeomorphism:
connected
Connected | Connected
identification
Identifications to Identifications
metrizable
5.1.1
path connected
Connected
simply connected
Simply | Simply
topologist's sine curve
Connected
torque
13.3.2.1 | 13.3.2.2 | 13.4.2.1
torus
2D | Higher | The | 4.4.2 | Two | A | Three
total differential
13.4.4 | 13.4.4 | 13.4.4 | 13.4.4
tower exponentiation
6.5.2
Towers of Hanoi
Exercises
trailers
13.1.2.4 to 13.1.2.4
trajectory
An
trajectory optimization
14.7 | 14.7 to 14.7
trajectory planning
Trajectory | Trajectory to Trajectory
path-constrained
14.6.3 to 14.6.3.5
transcription
14.7
transfer mode
Stable
transfer path
Stable
transformations
2D chain
3.3.1 to Homogeneous
2D rigid body
3.2.2 to Combining
3D chain
3.3.2 to The
3D rigid body
3.2.3 to The
general concepts
3.2.1 to Defining
kinematic tree
3.4 to What
nonrigid
3.5 to Flexible
transit path
Stable
transition configurations (mode change)
Stable
translating a disc
Translation
trapped on a surface
13.1.3.4 to 13.1.3.4
Traveling Salesman Problem
7.6
tray tilting
11.5.4 to 11.5.4 | 12.5.2
triangle fan
3D
triangle inequality
5.1.1
triangle model
3D to 3D
triangle strip
3D
triangular enumeration
14.2.2.3
triangulation
Warning: | Simplicial | Triangulation | Triangulation to Triangulation | Further | 8.4.2
tricycle
13.1.2.1
trim trajectory
14.2.3
trivial operator
Layer-by-layer
trivial topology
Some
Turing machine
1.4.1 | 6.5.1
two-point boundary value problem
14.1.1 | 14.1.2.2 | 14.2.1 | 14.2.2.1 | 14.3 | Reverse-time | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.3 | 14.3.4 | 14.3.4 | 14.3.4 | 14.3.4 | 14.3.4 | 14.3.4 | 14.3.4 | 14.3.4 | 14.4.1.1 | Searching | Backward | Backward | 14.4.3 | Tree-based | Tree-based | Tree-based | Tree-based | Sampling-based | 14.7 | 14.7 | 14.7 | 14.7 | 15. | 15. | Classical | 15.5
Type EE contact
A | 3D
Type EV contact
Computing | Computing | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | Exercises
Type FV contact
A | 3D
Type VE contact
Computing | Computing | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | 4.3.3 | Exercises | Exercises
Type VF contact
A | 3D
Udupa
4.
uncertainty
brief overview
Overview to Uncertainty
due to partial predictability
Planning | 10.1 to Policy
due to sensing
Planning | 11. to 11.7.2 | 12. to Squeezing
underactuated system
13.1.2 | Underactuation | 14.2.2.1 | Underactuated to Underactuated
unicycle
13.1.2.3 to 13.1.2.3 | 13.2.4.1 to 13.2.4.3
uniform random
5.2.2
union-find algorithm
Grid | Generic
unique point
Algorithms
unit complex number
Using
unit quaternions
Quaternions
unknot
Simplifying
unsupervised classification
9.2.4.1
unvisited states
2.2.1
upper envelope
9.3.3.2
upper value of a game
utility function
9.5.1.3 to 9.5.1.3
utility of money
9.5.1.3 to 9.5.1.3
utility theory
9.5.1 | 9.5.1.1 to 9.5.1.3
vacuum cleaning
7.6
value iteration
2.3.1 | 2.3.1
backward
2.3.1.1 to 2.3.1.1
convergence issues
Convergence to Convergence
forward
2.3.1.2 to 2.3.1.2
relative
Solutions
with interpolation
8.5.2 to Continuous
van der Corput sequence
The to The | Infinite | Infinite | Dispersion | Low-discrepancy | Low-discrepancy | 5.3.4 | 5.3.4 | Generic
variation of a function
13.4.1.1
variety
4.4 | Varieties | Varieties to Varieties
for 2D chains
4.4.2 to Three
4.4.3 to 4.4.3
vector field
8.3.1 | Vector to Piecewise-smooth | Vector | Vector to Vector | 13.1.1.2
equilibrium point
Equilibrium
normalized
8.4.1.1
over a cell complex
8.4.2 to 8.4.2
piecewise-smooth
Piecewise-smooth to Piecewise-smooth
vector space
Vector | Vector | Vector to Vector
over
Vector
over
to Vector
of functions
Vector to Vector
velocity field
Vector | Vector to Vector
velocity-tuning method
7.1.3 to 7.1.3
vertex selection method
5.4.1 | 5.4.1 | 5.4.1 | 5.4.1 | 5.4.3 | Ariadne's | Expansive-space | Expansive-space | Random-walk | 5.5.1 | 5.5.1
vertical decomposition
6.2.2 to Algorithm | Singular to Singular | 7.1.3 | 7.1.3
3D
6.3.3 to 6.3.3
violation-free state
14.1.3.1
virtual human
Virtual
VisBug
Using
visibility polygon
12.2.2
visibility region
12.3.4
5.6.2
visibility sensor
Depth-mapping | 12.2.2
visibility skeleton
12.2.2
visibility-based pursuit-evasion
12.4 | 12.4.1 to 12.4.3
a sequence of hard problems
12.4.1
complete algorithm
12.4.2 to 12.4.2
problem formulation
12.4.1 to 12.4.1
variations
12.4.3 to 12.4.3
Voronoi diagram
Testing
Voronoi region
Testing | Low-discrepancy | Low-discrepancy | 5.3.3 | 5.3.3 | 5.3.3 | 5.3.3 | 5.3.3 | A
Voronoi vertex
Dispersion
wall clock
Odometry
wall following
Algorithms
warping a path
Simply
wavefront
Euclidean
wavefront propagation
Wavefront to Wavefront | 8.5.2.3
wavelet
Euclidean
way point
8.4.3
weak backprojection
Backprojections | Backprojections | 10.6.1 | Backprojections | Backprojections
weighted-region problem
General to General
Weiner process
13.5.1
Whitney's embedding theorem
Manifold | Identifications
with probability one
A
word (sequence of motion primitives)
15.3.1
world
3.1 | 13.3.1
world frame
Defining
worst-case analysis
9.2.2 | The | The
wrench (from mechanics)
13.3.3
yaw rotation
Yaw, | Yaw,
zero-sum game
9.3 | 9.3 to 9.3.3.2
matrix representation of
9.3.1 to 9.3.1