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Robotics. R&N: ch 25. based on material from Jean-Claude Latombe, Daphne Koller, Stuart Russell. sensors. environment. ?. agent. effectors. Agent. Robots  Physical sensors and effectors. Sensors. Sensors that tell the robot position/change of joints: odometers, speedometers, etc.

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Robotics

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Robotics l.jpg

Robotics

R&N: ch 25

based on material from Jean-Claude Latombe, Daphne Koller, Stuart Russell


Agent l.jpg

sensors

environment

?

agent

effectors

Agent

Robots  Physical sensors and effectors


Sensors l.jpg

Sensors

  • Sensors that tell the robot position/change of joints: odometers, speedometers, etc.

  • Force sensing. Enables compliant motion--robot just maintains contact with object (video: compliant)

  • Sonar. Send out sound waves and measure how long it takes for it to be reflected back. Good for obstacle avoidance.

  • Vision systems


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Effectors

  • Converts software commands into physical motion

  • Typically electrical motors or hydraulic/pneumatic cylinders

  • Two main types of effectors:

    • locomotion

    • manipulation


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Locomotion

  • Legs!

    • traditional (video: honda human)

    • Other types

      • Statically stable locomotion: can pause at any stage during its gate without falling

      • Dynamically stable locomotion: stable only as long as it keeps moving (video: hopper)

  • Still, wheeled or tread locomotion like Shakey is still most practical for typical environments

  • Other methods: reconfigurable robots, fish robots, snake-like robots. (video: mod-robot)


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Manipulation

  • Manipulation of objects

  • Typical manipulators allow for:

    • Prismatic motion (linear movement)

    • Rotary motion (around a fixed hub)

  • Robot hands go from complex anthromorphic models to simpler ones that are just graspers

    • (video: manipulation)

    • (video: heart surgery)


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Problems in Robotics

  • Localization and Mapping

  • Motion planning


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Localization: Where Am I?

  • Use probabilistic inference: compute current location and orientation (pose) given observations

At-1

At-1

At-1

Xt-1

Xt-1

Xt-1

Zt-1

Zt-1

Zt-1


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Motion Planning

  • Simplest task that a robot needs to accomplish

  • Two aspects:

    • Finding a path robot should follow

    • Adjusting motors to follow that path

  • Goal: move robot from one configuration to another


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Configuration space

  • Describe robot’s configuration using a set of real numbers

  • Flatland -- robot in 2D -- how to describe?

  • Degrees of freedom: a robot has k degrees of freedom if it can be described fully by a set of k real numbers

    • e.g. robot arm (slide)

  • Want minimum-dimension parameterization

  • Set of all possible configurations of the robot in the k-dimensional space is called the configuration space of the robot.


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Example

  • workspace for 2-D robot that can only translate, not rotate

  • configuration space describes legal configurations

    • free-space

    • obstacles

  • Configuration space depends on how big robot is—need reference point


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Path planning

  • Goal: move the robot from an initial configuration to a goal position

  • path must be contained entirely in free space

  • assumptions:

    • robot can follow any path (as long as avoids obstacles)

    • dynamics are completely reliable

    • obstacles known in advance

    • obstacles don’t move


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Assumption #1

  • robot can follow any path

  • what about a car?

  • degrees of freedom vs. controllable degrees of freedom

    • holonomic (same)

    • nonholonomic

    • (video: holonomic)


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Motion planning

  • reduces to problem of finding a path from an initial state to a goal in robot’s configuration space

  • why is this hard?


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Reformulate as discrete search

  • finely discretized grid

  • cell decomposition: decompose the space into large cells where each cell is simple, motion planning in each cell is trivial

  • roadmap (skeletonization) methods: come up with a set of major “landmarks” in the space and a set of roads between them


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Issues in Search

  • Complete

  • Optimality

  • Computational Complexity


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Motion planning algorithms

  • grid

  • cell decomposition

    • exact

    • approximate

  • roadmap (skeletonization) methods:

    • visibility graphs

    • randomized path planning


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Robotics: Summary

  • We’ve just seen a brief introduction…

  • Issues:

    • sensors, effectors

    • Locomotion, manipulation

  • Some problems:

    • Localization

    • Motion Planning

  • Lots more!!


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