Planning Algorithms 1st edition by Steven LaValle 9780511239342 0511239343

Planning Algorithms 1st edition by Steven LaValle – Ebook PDF Instant Download/Delivery.
9780511239342, 0511239343
Full download Planning Algorithms 1st edition  after payment

Product details:

ISBN 10: 0511239343
ISBN 13: 9780511239342 
Author: Steven M. LaValle

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the ‘configuration spaces’ of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms 1st Table of contents:

PART I Introductory Material
1 Introduction
1.1 Planning to plan
1.2 Motivational examples and applications

• Discrete puzzles, operations, and scheduling
• A motion planning puzzle
• An automotive assembly puzzle
• Sealing cracks in automotive assembly
• Moving furniture
• Navigating mobile robots
• Playing hide and seek
• Making smart video game characters
• Virtual humans and humanoid robots
• Parking cars and trailers
• “Wreckless” driving
• Flying through the air or in space
• Designing better drugs
• Perspective
  1.3 Basic ingredients of planning
• State
• Time
• Actions
• Initial and goal states
• A criterion
• A plan
  1.4 Algorithms, planners, and plans
  1.4.1 Algorithms
  1.4.2 Planners
  1.4.3 Plans
• Execution
• Refinement
• Hierarchical inclusion
  1.5 Organization of the book
• PART I: Introductory Material
• PART II: Motion Planning
• PART III: Decision-Theoretic Planning
• PART IV: Planning Under Differential Constraints

2 Discrete Planning
2.1 Introduction to discrete feasible planning
2.1.1 Problem formulation
2.2 Searching for feasible plans
2.2.1 General forward search
2.2.2 Particular forward search methods

• Breadth first
• Depth first
• Dijkstra’s algorithm
• A-star
• Best first
• Iterative deepening
  2.2.3 Other general search schemes
• Backward search
• Bidirectional search
  2.2.4 A unified view of the search methods
  2.3 Discrete optimal planning
  2.3.1 Optimal fixed-length plans
  2.3.1.1 Backward value iteration
  2.3.1.2 Forward value iteration
  2.3.2 Optimal plans of unspecified lengths
  2.3.3 Dijkstra revisited
  2.4 Using logic to formulate discrete planning
  2.4.1 A STRIPS-like representation
  2.4.2 Converting to the state-space representation
  2.5 Logic-based planning methods
  2.5.1 Searching in a space of partial plans
  2.5.2 Building a planning graph
• Planning graph definition
• Layer-by-layer construction
• Mutex conditions
• Plan extraction
  2.5.3 Planning as satisfiability
  Further reading
  Exercises

PART II Motion Planning
Overview of Part II: Motion planning

• Planning in continuous spaces

1. Implicit representations
2. Continuous discrete
   3 Geometric Representations and Transformations
   3.1 Geometric modeling
   3.1.1 Polygonal and polyhedral models

• Convex polygons
• Nonconvex polygons
• Defining a logical predicate
• Polyhedral models
  3.1.2 Semi-algebraic models
  3.1.3 Other models
• Nonconvex polygons and polyhedra
• 3D triangles
• Nonuniform rational B-splines (NURBS)
• Bitmaps
• Superquadrics
• Generalized cylinders
  3.2 Rigid-body transformations
  3.2.1 General concepts
• Transforming the robot model
• A parameterized family of transformations
• Defining frames
  3.2.2 2D transformations
• Translation
• Rotation
• Combining translation and rotation
  3.2.3 3D transformations
• 3D translation
• Yaw, pitch, and roll rotations
• Determining yaw, pitch, and roll from a rotation matrix
• The homogeneous transformation matrix for 3D bodies
  3.3 Transforming kinematic chains of bodies
  3.3.1 A 2D kinematic chain
• Attaching bodies
• Homogeneous transformation matrices for 2D chains
  3.3.2 A 3D kinematic chain
• Two screws
• The homogeneous transformation matrix
  3.4 Transforming kinematic trees
• Motivation
• Common joints for W = R2
• Junctions with more than two rotation axes
• Constraining parameters
• What if there are loops?
  3.5 Nonrigid transformations
• Linear transformations
• Flexible materials
  Further reading
  Exercises
  Implementations

4 The Configuration Space
4.1 Basic topological concepts
4.1.1 Topological spaces

• Closed sets
• Special points
• Some examples
• Continuous functions
• Homeomorphism: Making a donut into a coffee cup
  4.1.2 Manifolds
• Manifold definition
• Cartesian products
• 1D manifolds
• Identifications
• 2D manifolds
• Higher dimensional manifolds
  4.1.3 Paths and connectivity
• Paths
• Connected vs. path connected
• Simply connected
• Groups
• The fundamental group
  4.2 Defining the configuration space
  4.2.1 2D rigid bodies: SE(2)
• Matrix groups
• Special Euclidean group
• Interpreting the C-space
  4.2.2 3D rigid bodies: SE(3)
• Using complex numbers to represent SO(2)
• Quaternions
• Using quaternion multiplication
• Finding quaternion parameters from a rotation matrix
• Special Euclidean group
  4.2.3 Chains and trees of bodies
  4.3 Configuration space obstacles
  4.3.1 Definition of the basic motion planning problem
• Obstacle region for a rigid body
• Obstacle region for multiple bodies
• Definition of basic motion planning
  4.3.2 Explicitly modeling Cobs: The translational case
• A polygonal C-space obstacle
• Computing the boundary of Cobs
• A polyhedral C-space obstacle
  4.3.3 Explicitly modeling Cobs: The general case
• 3D rigid bodies
• Chains and trees of bodies
  4.4 Closed kinematic chains
  4.4.1 Mathematical concepts
• Fields
• Polynomials
• Varieties
  4.4.2 Kinematic chains in R2
• Two links
• A zero-dimensional variety
• A one-dimensional variety
• Three links
  4.4.3 Defining the variety for general linkages
  Further reading
  Exercises
  Implementations

5 Sampling-Based Motion Planning
5.1 Distance and volume in C-space
5.1.1 Metric spaces

• Lp metrics
• Metric subspaces
• Cartesian products of metric spaces
  5.1.2 Important metric spaces for motion planning
• Pseudometrics
  5.1.3 Basic measure theory definitions
  5.1.4 Using the correct measure
  5.2 Sampling theory
  5.2.1 Motivation and basic concepts
• Denseness
• A random sequence is probably dense
• The van der Corput sequence
  5.2.2 Random sampling
• Generating a random element of SO(3)
• Generating random directions
• Pseudorandom number generation
• Testing for randomness
  5.2.3 Low-dispersion sampling
• Dispersion definition
• Making good grids
• Infinite grid sequences
• Dispersion bounds
  5.2.4 Low-discrepancy sampling
• Asymptotic bounds
• Relating dispersion and discrepancy
• Low-discrepancy sampling methods
  5.3 Collision detection
  5.3.1 Basic concepts
• Distance between two sets
• Two-phase collision detection
  5.3.2 Hierarchical methods
  5.3.3 Incremental methods
  5.3.4 Checking a path segment
  5.4 Incremental sampling and searching
  5.4.1 The general framework
  5.4.2 Adapting discrete search algorithms
• Discretization
• Neighborhoods
• Obtaining a discrete planning problem
• Grid resolution issues
  5.4.3 Randomized potential fields
  5.4.4 Other methods
• Ariadne’s clew algorithm
• Expansive-space planner
• Random-walk planner
  5.5 Rapidly exploring dense trees
  5.5.1 The exploration algorithm
  5.5.2 Efficiently finding nearest points
• Exact solutions
• Approximate solutions
  5.5.3 Using the trees for planning
• Single-tree search
• Balanced, bidirectional search
• More than two trees
  5.6 Roadmap methods for multiple queries
  5.6.1 The basic method
• Generic preprocessing phase
• Selecting neighboring samples
• Query phase
• Some analysis
  5.6.2 Visibility roadmap
  5.6.3 Heuristics for improving roadmaps
• Vertex enhancement [519]
• Sampling on the Cfree boundary [22, 26]
• Gaussian sampling [134]
• Bridge-test sampling [468]
• Medial-axis sampling [458, 638, 971]Further reading
  Exercises
  Notions of completeness

6 Combinatorial Motion Planning
6.1 Introduction

• Representation is important
• Reasons to study combinatorial methods
• Warning: Some methods are impractical
• Roadmaps
  6.2 Polygonal obstacle regions
  6.2.1 Representation
  6.2.2 Vertical cell decomposition
• Defining the vertical decomposition
• General position issues
• Defining the roadmap
• Solving a query
• Computing the decomposition
• Plane-sweep principle
• Algorithm execution
  6.2.3 Maximum-clearance roadmaps
  6.2.4 Shortest-path roadmaps
  6.3 Cell decompositions
  6.3.1 General definitions
• Simplicial complex
• Singular complex
  6.3.2 2D decompositions
• Triangulation
• Cylindrical decomposition
  6.3.3 3D vertical decomposition
  6.3.4 A decomposition for a line-segment robot
• An approximate solution
• Radar maps
• Critical changes in cells
• Constructing the roadmap
• Complexity
  6.4 Computational algebraic geometry
  6.4.1 Basic definitions and concepts
• Tarski sentences
• The decision problem
• The quantifier-elimination problem
• Semi-algebraic decomposition
  6.4.2 Cylindrical algebraic decomposition
• Semi-algebraic projections are semi-algebraic
• Real algebraic numbers

People also search for Planning Algorithms 1st :

planning to go or planning on going
    
planning algorithms by steven m lavalle
    
path planning algorithms for autonomous vehicles
    
path planning algorithms for drones