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Mobile Robotics Julie Letchner Angeline Toh Mark Rosetta Fundamental Idea: Robot Pose 2D world (floor plan) 3 DOF Very simple model—the difficulty is in autonomy Major Issues with Autonomy Sensor Inaccuracy Movement Inaccuracy Environmental Uncertainty

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Presentation Transcript
mobile robotics

Mobile Robotics

Julie Letchner

Angeline Toh

Mark Rosetta

fundamental idea robot pose

Fundamental Idea: Robot Pose

2D world (floor plan)

3 DOF

Very simple model—the difficulty is in autonomy

major issues with autonomy

Major Issues with Autonomy

  • Sensor

Inaccuracy

Movement

Inaccuracy

  • Environmental

Uncertainty

problem one localization

Problem One: Localization

  • World map
  • Robot’s initial pose
  • Sensor updates

Given:

Find:

  • Robot’s pose as it moves
how do we solve localization

How do we Solve Localization?

Represent beliefs as a probability density

Markov assumption

Pose distribution at time t conditioned on:

pose dist. at time t-1

movement at time t-1

sensor readings at time t

Discretize the density by

sampling

localization foundation

Localization Foundation

At every time step t:

UPDATE each sample’s new location based on movement

RESAMPLE the pose distribution based on sensor readings

algorithms

Algorithms

Markov localization (simplest)

Kalman filters (historically most popular)

Monte Carlo localization / particle filters

Same: Sampled probability distribution

Basic update-resample loop

Different: Sampling techniques

Movement assumptions

localization s sidekick globalization

Localization’s Sidekick: Globalization

  • Localization without knowledge of start location

Credit to Dieter Fox for this demo

  • One step further: “kidnapped robot problem”
problem two mapping

Problem Two: Mapping

  • Robot
  • Sensors

Given:

Find:

  • Map of the environment

(and implicitly, the robot’s location as it moves)

simultaneous localization and mapping slam

Simultaneous LocalizationAnd Mapping (SLAM)

If we have a map:

We can localize!

If we can localize:

We can make a map!

circular error problem

Circular Error Problem

If we have a map:

We can localize!

NOT THAT SIMPLE!

If we can localize:

We can make a map!

how do we solve slam

Incorporate location/map uncertainties into a single model

  • Optimize robot’s exploratory path
  • Use geometry (especially indoors)

How do we Solve SLAM?

Credit to Sebastian Thrun for this demo

Major hurdle:

correlation problem

for the interested

Good overview papers by Sebastian Thrun:

“Probabilistic Algorithms in Robotics”, 2000

“Robotic Mapping: A Survey”, 2002

For the Interested

Stanford course: cs225B

Build a Markov Localization engine

Run it on Amigobots to play soccer

up next

Mobile robot example: Underwater robots

Localization is only useful if we’re mobile…

…so how do these robots move?

Up Next…

Emergent Behaviors

Mobile robots more powerful in groups…

…but localization is expensive…

…so what can we do without localization?