Mobile Robotics Mini Presentation
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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
Movement Inaccuracy
Environmental Uncertainty
Sensor
Inaccuracy
Problem One: Localization Given: World map Robot’s initial pose Sensor updates 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 sensor readings at time t
Discretize the density by
Localization Foundation At every time step t: UPDATE each sample’s new location based on movement
RESAMPLE readings
the pose distribution based on sensor
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
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 Given: Robot Sensors Find: Map of the environment (and implicitly, the robot’s location as it moves)
Simultaneous Localization And 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? Incorporate location/map uncertainties into a single model Optimize robot’s exploratory path Use geometry (especially indoors)
Major hurdle: correlation problem Credit to Sebastian Thrun for this
For the Interested Good overview papers by Sebastian Thrun: “Probabilistic Algorithms in Robotics”, 2000 “Robotic Mapping: A Survey”, 2002 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? Emergent Behaviors Mobile robots more powerful in groups… …but localization is expensive… …so what can we do without
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
Movement Inaccuracy
Environmental Uncertainty
Sensor
Inaccuracy
Problem One: Localization Given: World map Robot’s initial pose Sensor updates 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 sensor readings at time t
Discretize the density by
Localization Foundation At every time step t: UPDATE each sample’s new location based on movement
RESAMPLE readings
the pose distribution based on sensor
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
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 Given: Robot Sensors Find: Map of the environment (and implicitly, the robot’s location as it moves)
Simultaneous Localization And 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? Incorporate location/map uncertainties into a single model Optimize robot’s exploratory path Use geometry (especially indoors)
Major hurdle: correlation problem Credit to Sebastian Thrun for this
For the Interested Good overview papers by Sebastian Thrun: “Probabilistic Algorithms in Robotics”, 2000 “Robotic Mapping: A Survey”, 2002 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? Emergent Behaviors Mobile robots more powerful in groups… …but localization is expensive… …so what can we do without
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