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Research Update

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

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Research Update

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  1. Research Update Ruijie He Oct 11, 2007

  2. 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

  3. 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

  4. Sensor Uncertainty Sampling • “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

  5. Sensor Uncertainty Sampling

  6. 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

  7. Experimental Comparison

  8. Presentation plan

  9. 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

  10. Belief Roadmap Algorithm • Probabilistic roadmap (PRM) in information space

  11. Extending BRM to UKF • Original formulation [Prentice, Roy] employs EKF

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