robotic Paradigms

8/12/2019 robotic Paradigms

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Robot Control Paradigms

Introduction toMobile Robotics

Wolfram Burgard, Cyrill Stachniss, Maren

Bennewitz, Diego Tipaldi, Luciano Spinello


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Classical / Hierarchical Paradigm

! 70s! Focus on automated reasoning and knowledge

representation

! STRIPS (Stanford Research Institute Problem Solver):Perfect world model, closed world assumption

! Find boxes and move them to designated position

Sense Plan Act


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Stanford CART 73

Stanford AI Laboratory / CMU (Moravec)


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Classical ParadigmStanford Cart

1. Take nine images of the environment, identifyinteresting points in one image, and use other

images to obtain depth estimates.

2. Integrate information into global world model.3. Correlate images with previous image set to

estimate robot motion.4. On basis of desired motion, estimated motion,

and current estimate of environment, determinedirection in which to move.

5. Execute the motion.


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Classical Paradigm as Horizontal/Functional Decomposition

Sense Plan Act

Perception

M

odel

Plan

Ex

ecute

Moto

rControl

ActionSensing

Environment


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Reactive / Behavior-based Paradigm

Sense Act

! No models: The world is its own, bestmodel

! Easy successes, but also limitations! Investigate biological systems


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Reactive Paradigm asVertical Decomposition

Avoid obstacles

Wander

Explore

ActionSensing

Environment


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Characteristics of ReactiveParadigm

! Situatedagent, robot is integral part of theworld.

! No memory, controlled by what ishappening in the world.

! Tight couplingbetween perception andaction via behaviors.

! Only local, behavior-specific sensing ispermitted (ego-centricrepresentation).


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Behaviors

! are a direct mappingof sensoryinputs to a pattern of motor actionsthat are then used to achieve a task.

! serve as the basic building block forrobotics actions, and the overallbehavior of the robot is emergent.

! support good software designprinciples due to modularity.


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Subsumption Architecture

! Introduced by Rodney Brooks86.! Behaviors are networks of sensing and

acting modules (augmented finite

state machinesAFSM).! Modules are grouped into layers of

competence.

! Layers can subsumelower layers.! No internal state!


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Level 0: Avoid

Polar plot of sonars

Collide

Feel force Run away Turn

Forward

Sonar polarplot

force heading

halt

heading

encoders


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Level 1: Wander

Collide


Forward

Sonar polarplot

forceheading

halt

Wander Avoidforce

heading

s

modified

heading

heading

encoders


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Level 2: Follow Corridor

Collide


Forward

Sonar polarplot

force

halt

Wander Avoidforce

heading

to middle

s

modified

heading

LookStay in

middle

s

corridor

heading

Integrate

heading

encoders

distance, direction traveled


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Potential Field Methodologies

!Treat robot as particleacting under theinfluence of a potential field

! Robot travels along the derivative of thepotential

! Field depends on obstacles, desired traveldirections and targets

! Resulting field (vector) is given by thesummation of primitive fields

! Strength of field may change with distanceto obstacle/target


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Primitive Potential Fields

Uniform Perpendicular

Attractive Repulsive Tangential


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Corridor Following withPotential Fields

! Level 0(collision avoidance)is done by the repulsive fields of detectedobstacles.

! Level 1(wander)adds a uniform field.! Level 2(corridor following)

replaces the wander field by three fields(two perpendicular, one uniform).


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Characteristics of Potential Fields

! Suffer from local minima

! Backtracking!

Random motion to escape local minimum! Procedural planner s.a. wall following! Increase potential of visited regions! Avoid local minima by harmonic functions

Goal


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Characteristics of Potential Fields

! No preference among layers! Easy to visualize! Easy to combine different fields! High update rates necessary! Parameter tuning important


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Reactive Paradigm

!Representations?

! Good software engineering principles?! Easy to program?! Robustness?! Scalability?


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Hybrid Deliberative/ReactiveParadigm

Sense Act

! Combines advantages of previous paradigms! World model used for planning! Closed loop, reactive control

Plan


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Discussion

!Imagine you want your robot to performnavigation tasks, which approach would youchoose?

! What are the benefits of the behavior basedparadigm?


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Discussion

!Imagine you want your robot to performnavigation tasks, which approach would youchoose?

! What are the benefits of the behavior basedparadigm?

! Which approaches will win in the long run?

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robotic Paradigms