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Research Update. Ruijie He Oct 11, 2007. Path-planning for Indoor Quadrotor. Challenges No GPS Requires environmental sensors for state estimation Limited payload No SICK laser, range = 50m Hokuyo laser effective range = 3m - PowerPoint PPT Presentation
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Research Update
Ruijie He
Oct 11, 2007
Path-planning for Indoor Quadrotor
Challenges No GPS
Requires environmental sensors for state estimation
Limited payload No SICK laser, range = 50m Hokuyo laser effective range = 3m
Control inputs without sensor measurements are highly unreliable for state estimation
Efficient Sampling in Belief Space
Family of PRM methods Samples C space to represent free space Typically uses uniform sampling
Belief Space Planning Account for uncertainty in state estimation BRM – Covariance update in 1-step update
Need efficient sampling strategy High dimension space BRM computation
Expensive covariance calculations
“Sensor Uncertainty Field” (SUF) Takeda and Latombe Estimates expected localization error at each point
Information gain: Entropy: UKF unscented transform
Probability of getting sensor measurement at each sigma pt
Sensor Uncertainty Sampling
Sensor Uncertainty Sampling
Experimental results – BRM-SUS vs. PRM
Plan paths using respective algorithms and sampling strategies Execute planned trajectories using joystick, collecting laser messages and
joystick commands Performed UKF localization using sensor measurements and control
inputs Compared ability to localize position accurately
Experimental Comparison
Presentation plan
Motivation Search and rescue operation
Chemical attack Indoor environment with debris Want a flying robot to autonomously navigate to
goal position in given map Challenges
No GPS Very limited payload
Paper contributions Extending Belief Roadmap (BRM) to use UKF Efficient sampling strategy to perform BRM search,
using concept of sensor uncertainty
Probabilistic roadmap (PRM) in information space
Belief Roadmap Algorithm
Extending BRM to UKF
Original formulation [Prentice, Roy] employs EKF