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Motion planning: Applications, Algorithms & Concepts

Motion planning, also path planning (also known as the navigation problem or the piano mover's problem) is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. The term is used in computational geometry, computer animation, robotics and computer games.

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Motion planning topic overview

The analysis highlights Applications, Algorithms and Concepts as prominent areas in the source structure around Motion planning.

Related topics: 64
Source areas: 7
Connected nodes: 71
Extracted relationships: 69
Concept neighborhoods: 22
Bridge connections: 71

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Algorithms · 28 topics

Overview · 14 topics

Concepts · 8 topics

Problem variants · 6 topics

Applications · 4 topics

Completeness and performance · 2 topics

Motion-planning concepts · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Suggested research paths

A focused starting point derived from the topic graph, ranked independently of the source article order.

Start with these areas

Less obvious directions

Motion-planning concepts

Dubins path Velocity obstacle

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Computational problem
Computational geometry
Computer animation
Robotics
Computer games Computer game
Mobile robot
Autonomy Autonomous robot
Automation
CAD software
Digital characters Digital character
Video game
Architectural design
Robotic surgery
Biological molecules Biological molecule

Concepts

Configuration space Configuration space (physics)
Special orthogonal group
Euler angles
Gimbal lock
Quaternions Quaternions and spatial rotation
Other Rotation matrices
Forward kinematics
Collision detection

Algorithms

Intractable Computational complexity theory
Legged robots Legged robot
Search algorithms
A* A* search algorithm
Any-angle path planning
Incremental heuristic search
Subpavings Subpaving
Set inversion problem Set inversion
Interval analysis Interval arithmetic
Dijkstra's algorithm
Nicolas Delanoue Nicolas Delanoue?action=edit&redlink=1
Visibility graph
Cell decomposition Boustrophedon cell decomposition
Voronoi diagram
Minkowski sum
Local minima
Navigation function
Quasirandom
Pseudorandom
Markov chain Monte Carlo
Random walk
Bug algorithm
D*
Dynamic window approach
Probabilistic roadmap
Rapidly-exploring random tree
Theta*
Vector Field Histogram

Motion-planning concepts

Completeness and performance

Problem variants

Holonomic Holonomic constraints
Nonholonomic
Hybrid systems
Mechanical assembly Design for assembly
Reconfigurable robots Self-Reconfiguring Modular Robotics
Networked control systems

Applications

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Motion planning connects Entity context

The extracted context around Motion planning shows recurring relationship patterns in the source. For example, Motion planning → Algorithms, April, Berg, Burgard, Business Media, Cambridge University Press, Chapter, Choset, Computational Geometry, Hutchinson, Implementation, ISBN, Jean-Claude, Kantor, Kavraki, Kreveld, Latombe, LaValle, Lynch, Marc Another extracted example is Motion planning → April, Control, Environment, Jean-Claude Latombe, Motion Planning Kit, Motion Strategy Library, OMPL, Open Motion Planning Library, Open Robotics Automation Virtual, Robot Motion Planning, Simox. Use these groups to spot repeated connection types before inspecting the individual relationships.

Motion planning

Top relations

related to Further reading · 33

Motion planning → Algorithms, April, Berg, Burgard, Business Media, Cambridge University Press, Chapter, Choset, Computational Geometry, Hutchinson, Implementation, ISBN, Jean-Claude, Kantor, Kavraki, Kreveld, Latombe, LaValle, Lynch, Marc

related to External links · 11

Motion planning → April, Control, Environment, Jean-Claude Latombe, Motion Planning Kit, Motion Strategy Library, OMPL, Open Motion Planning Library, Open Robotics Automation Virtual, Robot Motion Planning, Simox

see also · 10

Motion planning → Class, Computational, Gimbal, LibraryOpenRAVE, Mathematical, Multi-robot, Pathfinding, Plotting, Similar, The Open Motion Planning

related to Algorithms · 6

Motion planning → Cfree, Exact, Low-dimensional, Potential-field, Sampling-based, They

related to Target space · 4

Motion planning → However, In, Target, To

related to Concepts · 1

Motion planning → The

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

robot planning space motion configuration path algorithms cfree problem one complete grid time approaches find algorithm number grid-based using problems

Motion planning relationships Subject–Predicate–Object triples

TTTA extracted 69 structured relationships around Motion planning. Examples in this analysis include Dijkstra or A → instance of → a neighbor graph is built and paths can be found using algorithms and counting its number of connected components.Geometric algorithmsPoint robots among polygonal obstaclesVisibility graphCell decompositionVoronoi diagramTranslating objects among obstaclesMinkowski sumFinding the way out of a buildingfarthest ray traceGiven a bundle of rays around the current position attributed with their length hitting a wall → instance of → avoiding two small horizontal segments.Nicolas Delanoue has shown that the decomposition with subpavings using interval analysis also makes it possible to characterize the topol…. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
Dijkstra or A	instance of	a neighbor graph is built and paths can be found using algorithms	0.80	text
counting its number of connected components.Geometric algorithmsPoint robots among polygonal obstaclesVisibility graphCell decompositionVoronoi diagramTranslating objects among obstaclesMinkowski sumFinding the way out of a buildingfarthest ray traceGiven a bundle of rays around the current position attributed with their length hitting a wall	instance of	avoiding two small horizontal segments.Nicolas Delanoue has shown that the decomposition with subpavings using interval analysis also makes it possible to characterize the topol…	0.80	text
the robot moves into the direction of the longest ray unless a door is identified	instance of	avoiding two small horizontal segments.Nicolas Delanoue has shown that the decomposition with subpavings using interval analysis also makes it possible to characterize the topol…	0.80	text
counting its number of connected components	instance of	avoiding two small horizontal segments.Nicolas Delanoue has shown that the decomposition with subpavings using interval analysis also makes it possible to characterize the topol…	0.80	text
Motion planning	related to Algorithms	Low-dimensional	0.60	section
Motion planning	related to Algorithms	Cfree	0.60	section
Motion planning	related to Algorithms	Exact	0.60	section
Motion planning	related to Algorithms	Potential-field	0.60	section
Motion planning	related to Algorithms	Sampling-based	0.60	section
Motion planning	related to Algorithms	They	0.60	section
Motion planning	related to Concepts	The	0.60	section
Motion planning	related to External links	Open Robotics Automation Virtual	0.60	section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Motion planning bring nearby vocabulary together. In this analysis, examples include Planning, Robot and Robots. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

Motion planning
- Planning
- Robot
- Robots
- Path
- Problem
- Algorithms
- Robotics
- Applications
- Fields
- Obstacle
- Space
- Target
motion planning
- Planning
- Robot
- Robots
- Path
- Problem
- Algorithms
- Robotics
- Applications
- Fields
- Obstacle
- Space
- Target
configuration space
- Space
- Grid-based
- Grid
- Obstacle
- Target
- High-dimensional
- Robot
- Point
- Search
- Number
- Problems
- Approaches
search algorithms
- Problems
- Approaches
- Problem
- Motion
- Planning
- Robots
- Grid
- Search
- Configuration
- Space
- Applications
- Fields
any-angle path planning
- Planner
- Robot
- Robots
- Planning
- Search
- Problem
- Algorithms
- Robotics
- Applications
- Space
- Obstacle
- Roadmap
dubins path
- Planner
- Planning
- Search
- Obstacle
- Roadmap
- Approaches
- Problem
- Configuration
- One
- Space
- Algorithms
- Cfree
algorithms
- Problems
- Problem
- Motion
- Planning
- Robots
- Search
- Configuration
- Applications
- Fields
- High-dimensional
- Potential
- Path
mobile robot
- Robots
- Point
- Space
- Configuration
- Robotics
- Target
- Applications
- Fields
- Cannot
- Obstacle
- Using
- Problem

Connections between topic areas Semantic bridges

For Motion planning, one of the stronger structural bridges in this analysis connects Motion planning with Algorithms. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Motion planning — Algorithms · splits 43 ⟂ 29

Motion planning — Overview · splits 57 ⟂ 15

Motion planning — Concepts · splits 63 ⟂ 9

Motion planning — Problem variants · splits 65 ⟂ 7

Motion planning — Applications · splits 67 ⟂ 5

Motion planning — Motion-planning concepts · splits 69 ⟂ 3

Motion planning — Completeness and performance · splits 69 ⟂ 3

Map overview Semantic statistics

Motion planning

Nodes72

Edges71

Triples69

Avg. degree1.97

Density0.027778

Components1

Source & methodology

TTTA analyzes the structure around Motion planning to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Algorithms & Concepts, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Motion planning · EN edition · Analysis: TopicsToTalkAbout

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Motion planning topic overview

What this topic covers Research coverage

Key facts & relationships

Suggested research paths

Start with these areas

Algorithms

Applications

Concepts

Problem variants

Completeness and performance

Less obvious directions

Motion-planning concepts

Explore all related topics Closing gaps

Overview

Concepts

Algorithms

Motion-planning concepts

Completeness and performance

Problem variants

Applications

Advanced semantic analysis

How Motion planning connects Entity context

Motion planning

Top relations

Important terminology

Important terminology

Motion planning relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Motion planning

Source & methodology