Collision Free Navigation Overview

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Overview

- Search strategy - Classification

- General control scheme - Navigation hierarchy - Perception and sensors - Localization and trajectory - Path planning

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10/04/22 Manfred Stenzel Cognitive Robotics SS2010 2

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Search Strategy

Keywords KeywordsKeywords

Keywords: : : : collision avoidance, Kollisionsvermeidung, robot navigation Sources

SourcesSources

Sources and and and and resultsresultsresultsresults::::

– Google: keyword search, overview, > 1000 articles – Amazon: english books, > 1000 books

– OPAC: collision avoidance (3 books), robot navigation (22 books), specialized articles

– citeseerX: > 10000 specialized articles Internet Internet Internet Internet lecture notes as main sourceslecture notes as main sourceslecture notes as main sourceslecture notes as main sources!!!!

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Main

Main Sources

Sources

Siegwart, Siegwart,Siegwart,

Siegwart, ScaramuzzaScaramuzzaScaramuzzaScaramuzza, , , , ETH Zürich, SS2010, Master Course: 151-0854-00L, Autonomous Mobile Robots

Guo GuoGuo

Guo, , , , Stevens Institute of Technology Hoboken, SS2010, EE631 Cooperating Autonomous Mobile Robots

Khamis KhamisKhamis

Khamis, , , , University of Waterloo, WS2007/8, CSEN904: Introduction to Robotics

Franz, Mallot, Franz, Mallot, Franz, Mallot,

Franz, Mallot, MPI Tübingen, 2000, Biomimetic Robot Navigation Stachniss

StachnissStachniss

Stachniss, , , , diploma thesis, 2002, Zielgerichtete Kollisionsvermeidung für mobile Roboter in dynamischen Umgebungen, dissertation, 2006, Exploration and Mapping with Mobile Robots

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Classification

Classification ((((wikipedia

wikipedia

wikipedia....org

org

org))))

org

Mobile and Mobile and Mobile and

Mobile and autonomous autonomous autonomous autonomous robot:robot:robot:robot:

– A mobile robotmobile robotmobile robotmobile robot is an automatic machine that is capable of movement in a given environment

– Autonomous robotsAutonomous robotsAutonomous robots are robots which can perform desired tasks inAutonomous robots unstructured environments without continuous human guidance

No simulation and games; no manipulators; focus on ground based vehicles

Collision CollisionCollision Collision

– Isolated event in which two or more moving bodies (colliding

bodies) exert relatively strong forces on each other for a relatively short time

No physical contact with moving or static obstacles Navigation

NavigationNavigation Navigation

– Process of determining and maintaining a course or trajectory to a goal location (Franz, Mallot, 2000)

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General

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Mobile Robot Basics

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Sensors

Sensors Classification

Classification

Collision avoidance Collision avoidanceCollision avoidance Collision avoidance:::: - Proximity, sonics,...

Localisation LocalisationLocalisation

Localisation and and and and mapbuildingmapbuildingmapbuildingmapbuilding::::

- Relative position measurements (dead-reckoning): odometry,... - Absolute position measurements

(reference-based systems): magnetic compass, active beacon, GPS, landmark navigation,...

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Raw Data

Raw Data and Information

and Information

and Information Extraction

Extraction

Extraction ---- Example Lego

Extraction

Example Lego

Example Lego Sonar

Sonar

Range 6 - 255 cm +/- 3 cm , 1cm discrete, angle +140/-130 degree, timing 4ms

Failures FailuresFailures

Failures:::: calibration, range, angle, surface, reflection, misleading signals from other sources Failure limitation

Failure limitationFailure limitation

Failure limitation ((((informationinformationinformationinformation extraction

extractionextraction

extraction):):):): averaging, exponential smoothing, interpolation

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Navigation

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Search

- Goal found only by chance - Basic competencies:

- Locomotion - Goal detection

- Good for backup strategy - Uneffective

- Robot example: Randomized movement with collision

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Direction

Direction––––following

following

following and

following

and

and Path

Path

Path Integration

Path

Integration

Direction DirectionDirection

Direction----followingfollowingfollowingfollowing::::

- Align course with locally available direction

- Goal need not to be perceivable - Direction information from

external or internal reference

- Misses goal, if displaced from trail Path integration

Path integrationPath integration Path integration::::

- Additional use of odometry for distance information

- Not confined to fixed trail - Ability to return to start

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Aiming

- Body is oriented to goal - Goal is cued (beacon,

olfactory,...)

- Approach from various directions without cumulative error

- Only perceivable in between „catchment area“

- Robot example: Light loving vehicle (Braitenberg)

- Nature: phase difference detection of crickets

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Guidance

- Guidance by spatial configuration of surrounding objects

- Relationship between current location, goal and currently perceptible environment memorized

- Nature: Bees – panoramic snapshot

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Recognition

Recognition----Triggered

Triggered

Triggered Response

Response

- Connection of two locations by means of a local navigation method

- Recognition of starting location triggers activation of local

navigation method leading - Learning of sensory patterns

between start and action

- Each goal needs it‘s own route - No planning, knowledge limited to

next action

- Elementary step for building routes

- Need of search strategy if route segment is blocked

- Nature: Ants learn always to pass a landmark on the right side

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Topological

Topological Navigation

Navigation

- Use of same representation for multiple goals

- Representation as a graph (route integration)

- Planning capabilities

- No generation of novel routes over unvisited terrain

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Survey

Survey Navigation

Navigation

- Embedding of all known places and of their spatial relations into a common frame of reference

- Inference of relationship of arbitrary places

- Ability to find novel paths over unknown terrain

Cognitive maps Cognitive mapsCognitive maps Cognitive maps::::

- Change from stereotyped to flexible behaviors

- Use of goal-independent

memories for different routes and planning

- „Latent“ (i. e. driven by curiosity) learning

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Localisation

Localisation and

and

and Trajectory

Trajectory

3 degrees of freedom for mobile robot, 2 DOF if holonomic drive (turns on point)

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Path Planning Approaches

Requirements RequirementsRequirements Requirements::::

- Existing static map with static/moving obstacles - Quick calculation (< 250ms)

- Collision free trajectory with uncertainty of sensing and kinematics Approaches

ApproachesApproaches Approaches::::

- Conversion from continuous to discrete configuration space - Use of basic algorithms (A*, D*, Value Iteration)

- Optimization of calculation with static trajectory and local obstacle avoidance

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Roadmaps

Roadmaps ((((Lacombe

Lacombe

Lacombe 1991)

1991)

- Avoid searching the entire space - Precompute graphs (roads) to the

goal along the obstacles

- Find (shortest) path between start and goal using the roads

- VisibilityVisibilityVisibilityVisibility GraphGraphGraphGraph tries to stay as close as possible to obstacles, any execution error will lead to a collision

- Voronoi diagramVoronoi diagramVoronoi diagramVoronoi diagram is not

necessarily the best heuristic (“stay away from obstacles“)

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Cellular Decomposition

Cellular Decomposition (Schwarz and

(Schwarz and

(Schwarz and Sharir

Sharir

Sharir 1983)

1983)

- Define discrete grid in configuration space

- Mark any cell of grid that intersects with obstacle as blocked

- Find path through remaining cells - If no path found: subdivide mixed

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Potential Fields (

Potential Fields (Khatib

Khatib

Khatib 1986)

1986)

- Stay away from obstacles (repulsive field)

- Move closer to goal (attractive field)

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Reflexive

Reflexive Obstacle Avoidance

Obstacle Avoidance

- Principle: sense – decide – act - Low level representation

- Simplified decision-making for fast reaction

- Combination with higher level behaviors

- Basic example: stop moving if sensor detection

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Obstacle Avoidance

Obstacle Avoidance ((((Local Path Planning

Local Path Planning

Local Path Planning))))

Local Path Planning

- The goal of the obstacle

avoidance algorithms is to avoid collisions with obstacles

- It is usually based on local map - Often implemented as a more or

less independent task

- However, efficient obstacle

avoidance should be optimal with respect to

- The overall goal

- the actual speed and kinematics of the robot - the on boards sensors

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Obstacle Avoidance

Obstacle Avoidance: Bug1

: Bug1

- Following along the obstacle to avoid it

- Each encountered obstacle is once fully circled before it is left at the point closest to the goal

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Obstacle Avoidance

Obstacle Avoidance: Bug2

: Bug2

- Following the obstacle always on the left or right side

- Leaving the obstacle, if the direct connection between start and goal is crossed

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Dynamic Window

Dynamic Window Approach (Fox et al 1997)

Approach (Fox et al 1997)

- Separation of path planning (static, global) from collision avoidance (local)

- Consideration of robot kinematics by searching a well chosen

velocity space („navigation function“):

- Circular trajectories uniquely determined by pairs (v,⍹) of translational and rotational velocities.

- Admissible velocity, if robot stops before closest

obstacle

- Dynamic window restricts admissible velocities to the limited accelerations of the robot