**(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
**accessibility (of a roadmap)**- Roadmaps
**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
**Adams methods**- Multistep
**adding integrators to a model**- 13.2.4 to 13.2.4.3
**adjoint transition equation**- 15.2.3
**adjoint variables**- 13.4.4 | 15.2.3
**admissible configurations**- Admissible
**affine space**- Varieties
**airport terminal**- Navigation
**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
**Ariadne's Clew algorithm**- Ariadne's to Ariadne's
**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**- Fixed-roadmap
**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

**bad bracket**- 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

**Basu-Pollack-Roy roadmap algorithm**- 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
**breadth-first search**- Breadth
**bridge-test sampling**- Bridge-test to Bridge-test
**broad-phase collision detection**- 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
**Candorcet paradox**- 9.5.1.2
**Canny**- 6.4.3
**Canny's roadmap algorithm**- 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
**causal links**- 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
**broad-phase**- 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**- 6.1 to Roadmaps
**polygonal case**- 6.2 to 6.2.4

**combinatorial roadmaps**- 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
**connectivity-preserving roadmap**- Roadmaps
**connector in a roadmap**- 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
**continuous Dijkstra paradigm**- 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
**quadratic**- 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
**dead states**- 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

**directed roadmap**- 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
**of a two-link manipulator**- 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
**fixed-roadmap coordination**- Fixed-roadmap to Planning
**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
**breadth-first**- Breadth to Breadth
**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
**ladder-nested**- 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

**gap navigation tree**- 12.3.4 to I-space
**gap sensor**- Depth-mapping
**gap theorems**- Real
**garage configuration**- Fixed-roadmap
**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
**gingerbread face**- 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
**gradient descent**- Navigation | 8.4.1.2 | 8.4.4.3
**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
**navigation function on**- Navigation to Navigation | 8.2.3 to Other
**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
**guard in a roadmap**- 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****adapting search algorithms**- 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
**junction of links**- 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**- Navigation | 8.4.4.3
**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
**Lennard-Jones radii**- 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
**linear-quadratic problems**- 15.2.2 to 15.2.2
**linear-quadratic-Gaussian (LQG) system**- 11.6.1 | 15.2.2
**link**- 3.3
**linkage**- 3.3
**linkage graph**- 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**- 9.3.2 | 10.5.1.1 | Saddle
**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-clearance navigation function**- Maximum to Maximum
**maximum-clearance roadmap**- 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
**minimum turning radius**- 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
**admissible**- 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
**navigation function**- 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

**navigation problem**- 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
**NF2 (a navigation function)**- 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
**outdoor navigation**- 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**- Admissible
**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
**radial sweep**- 6.2.4 | 8.4.3

**planetary navigation**- 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**- Navigation
**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
**quadratic cost functional**- 15.2.2
**quadratic potential function**- 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
**radar map**- Radar | Radar to Radar
**radial sweep**- 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

**randomized saddle point**- 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**- Navigation | 8.4.4.3
**risk****conditional Bayes'**- Optimal
**frequentist**- 9.5.2.1

**roadmap****directed**- Sampling-based
**general requirements**- Roadmaps to Roadmaps

**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

**sampling-based roadmap****-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
**visibility roadmap**- 5.6.2 to 5.6.2

**sampling-based roadmaps**- 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
**adaptation to continuous spaces**- 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
**saddle point**- 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

**shadow component**- 12.3.4
**shadow region**- 12.3.4
**shearing transformation**- Linear
**shooting methods**- 14.7
**shortest-path functional**- 13.4.1.1 to 13.4.1.1
**shortest-path roadmap**- 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
**Simpson paradox**- 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
**superquadric**- Superquadrics
**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**- 9.3.2 | 10.5.1.1 | Saddle
**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
**for general linkages**- 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
**visibility roadmap**- 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
**randomized saddle point**- 9.3.3.1 to 9.3.3.2
**randomized value of**- 9.3.3.1
**regret in**- Regret to Regret
**saddle point**- Saddle | Saddle to Saddle
**value of**- Saddle

Steven M LaValle 2012-04-20