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