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# Mobile Robotics - PowerPoint PPT Presentation

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

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

### Problem One: Localization

• World map

• Robot’s initial pose

Given:

Find:

• Robot’s pose as it moves

### 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

Discretize the density by

sampling

### 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

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 without knowledge of start location

Credit to Dieter Fox for this demo

• One step further: “kidnapped robot problem”

### Problem Two: Mapping

• Robot

• Sensors

Given:

Find:

• Map of the environment

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

### Simultaneous LocalizationAnd Mapping (SLAM)

If we have a map:

We can localize!

If we can localize:

We can make a map!

### 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?

Credit to Sebastian Thrun for this demo

Major hurdle:

correlation problem

“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

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?