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# Mobile Robotics

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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## Mobile Robotics

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### Presentation Transcript

1. Mobile Robotics Julie Letchner Angeline Toh Mark Rosetta

2. Fundamental Idea: Robot Pose 2D world (floor plan) 3 DOF Very simple model—the difficulty is in autonomy

3. Major Issues with Autonomy • Sensor Inaccuracy Movement Inaccuracy • Environmental Uncertainty

4. Problem One: Localization • World map • Robot’s initial pose • Sensor updates Given: Find: • Robot’s pose as it moves

5. 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

6. Localization Foundation At every time step t: UPDATE each sample’s new location based on movement RESAMPLE the pose distribution based on sensor readings

7. 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

8. Localization’s Sidekick: Globalization • Localization without knowledge of start location Credit to Dieter Fox for this demo • One step further: “kidnapped robot problem”

9. Problem Two: Mapping • Robot • Sensors Given: Find: • Map of the environment (and implicitly, the robot’s location as it moves)

10. Simultaneous LocalizationAnd Mapping (SLAM) If we have a map: We can localize! If we can localize: We can make a map!

11. Circular Error Problem If we have a map: We can localize! NOT THAT SIMPLE! If we can localize: We can make a map!

12. 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

13. 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

14. 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?

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