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Autonomous Robot Navigation

Autonomous Robot Navigation. Panos Trahanias e-mail: trahania@csd.uoc.gr. ΗΥ475 Fall 2007. Mobile Robots - Examples. The museum tour-guide Minerva. The Mars rover Sojourner. The museum tour-guide Lefkos. The RHex Hexapod. Typical Mobile Robot Setup. Sensors Stereo vision Sonars

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Autonomous Robot Navigation

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  1. Autonomous Robot Navigation Panos Trahanias e-mail: trahania@csd.uoc.gr ΗΥ475 Fall 2007

  2. Mobile Robots - Examples The museum tour-guide Minerva The Mars rover Sojourner The museum tour-guide Lefkos The RHex Hexapod Panos Trahanias: Autonomous Robot Navigation

  3. Typical Mobile Robot Setup Sensors Stereo vision Sonars Bump sensors Infrared sensors Laser scanner Bump sensors Sonars Odometry Interaction Processing Power Motors Communications Panos Trahanias: Autonomous Robot Navigation

  4. Scope of the Course Mobile Robots – How to move and achieve motion target goals in (indoor) environments Hence • Localization (where am I?) • Mapping, simultaneous localization and mapping – SLAM (what is my workspace?) • Path planning (how to get there?) • Obstacle avoidance (… get there safely…) Panos Trahanias: Autonomous Robot Navigation

  5. C G Autonomous Navigation- Research Directions • Given • An environment representation - Map • Knowledge of current position C • Target position G • A path has to be planned and tracked that will take the robot from C to G Panos Trahanias: Autonomous Robot Navigation

  6. C G O Autonomous Navigation- Research Directions • During execution (run-time) • Objects / Obstacles O may block the robot • The planned path is no-longer valid X • The obstacle needs to be avoided and the path may need to be re-planned Panos Trahanias: Autonomous Robot Navigation

  7. Important questions (Levitt et al ’91) Important navigation issues Navigation Issues Where am I Robot localization Where are other places relative to me Map building How do I get to other places from here Path/motion planning Panos Trahanias: Autonomous Robot Navigation

  8. Navigation Issues – Underlying HW Sensors Stereo vision Sonars Bump sensors Infrared sensors Laser scanner Bump sensors Sonars Odometry Interaction Processing Power Motors Communications Laser Scanner Panos Trahanias: Autonomous Robot Navigation

  9. Range Sensor Model • Laser Rangefinder • Model range and angle errors. Panos Trahanias: Autonomous Robot Navigation

  10. Need for Modeling Robot + Environment Need for Appropriate Modeling Extremely Complex Dynamical System Panos Trahanias: Autonomous Robot Navigation

  11. State depends only on previous state and observations Static world assumption Hidden Markov Model (HMM) Markov Assumption Bayesian estimation: Attempt to construct the posterior distribution of the state given all measurements Panos Trahanias: Autonomous Robot Navigation

  12. A Dynamic System • Most commonly - Available: • Initial State • Observations • System (motion) Model • Measurement (observation) Model Panos Trahanias: Autonomous Robot Navigation

  13. Localization (inference task)Compute the probability that the robot is at pose z at time t given all observations up to time t (forward recursions only) Inference - Learning • Map building (learning task)Determine the map m that maximizes the probability of the observation sequence. Panos Trahanias: Autonomous Robot Navigation

  14. Discrete representation Grid (Dynamic) (Dynamic) Markov localization (Burgard98) Samples Monte Carlo localization (Fox99) Continuous representation Gaussian distributions Kalman filters (Kalman60) Belief State How is the posterior distribution calculated? How is the prior distribution represented? Panos Trahanias: Autonomous Robot Navigation

  15. Example: State Representations for Robot Localization Discrete Representations Continuous Representations Grid Based approaches (Markov localization) Particle Filters (Monte Carlolocalization) Kalman Tracking Panos Trahanias: Autonomous Robot Navigation

  16. LOCALIZATION Panos Trahanias: Autonomous Robot Navigation

  17. Markov Assumption Localization: determine the likelihood of robot’s state Given a sequence of observations Determine the probability Panos Trahanias: Autonomous Robot Navigation

  18. Markov Assumption In practice: too difficult to determine the joint effect of all observations up to time K. Common assumption: hidden states obey the Markov assumption (static world assumption), so as we can factor as Panos Trahanias: Autonomous Robot Navigation

  19. Markov Assumption Panos Trahanias: Autonomous Robot Navigation

  20. Markov Assumption All information about past history is represented in Integrate over all possible states Different approaches in this representation lead to different treatments of the problem. Panos Trahanias: Autonomous Robot Navigation

  21. Kalman Filtering Probabilistic estimation • Simultaneously maintain estimates for both the state x and error covariance matrix P • Equivalent to say: output of a Kalman filter is a Gaussian PDF (other methods can handle more general distributions) Panos Trahanias: Autonomous Robot Navigation

  22. Kalman Filtering Crude localization method: integrate robot velocity commands Problem: info continuously lost, no new info added. Solution: add info from exterioreceptive sensors. Panos Trahanias: Autonomous Robot Navigation

  23. Kalman Filtering Sensor measurements add new info – PDF in sensor space. Localization knowledge (prior to sensor measurement) is a PDF in state space. Probabilistic Estimation: merge the 2 PDFs Two step process: prediction update Panos Trahanias: Autonomous Robot Navigation

  24. Kalman Filtering Simple observer update Panos Trahanias: Autonomous Robot Navigation

  25. Kalman Filtering Prediction Update Panos Trahanias: Autonomous Robot Navigation

  26. Kalman Filtering Observing with probability distributions Panos Trahanias: Autonomous Robot Navigation

  27. Kalman Filtering Prediction Update where Panos Trahanias: Autonomous Robot Navigation

  28. Kalman Filtering Panos Trahanias: Autonomous Robot Navigation

  29. Kalman Filtering Panos Trahanias: Autonomous Robot Navigation

  30. Kalman Filtering Panos Trahanias: Autonomous Robot Navigation

  31. Kalman Filtering Panos Trahanias: Autonomous Robot Navigation

  32. Bayesian Methods Panos Trahanias: Autonomous Robot Navigation

  33. Bayesian Methods – Discrete Approximations Panos Trahanias: Autonomous Robot Navigation

  34. Bayesian Methods – Discrete Approximations Panos Trahanias: Autonomous Robot Navigation

  35. Bayesian Methods – Discrete Approximations Results Panos Trahanias: Autonomous Robot Navigation

  36. Bayesian Methods – Discrete Approximations Results Panos Trahanias: Autonomous Robot Navigation

  37. Bayesian Methods – Particle Filters Panos Trahanias: Autonomous Robot Navigation

  38. Bayesian Methods – Particle Filters/Resampling Panos Trahanias: Autonomous Robot Navigation

  39. Bayesian Methods – Particle Filters Motion Model Panos Trahanias: Autonomous Robot Navigation

  40. Bayesian Methods – Particle Filters State Belief Panos Trahanias: Autonomous Robot Navigation

  41. Bayesian Methods – Particle Filters Global Localization Panos Trahanias: Autonomous Robot Navigation

  42. Bayesian Methods – Particle Filters Global Localization - Results Panos Trahanias: Autonomous Robot Navigation

  43. Bayesian Methods – Sensor Models Typical Sonar Scan Panos Trahanias: Autonomous Robot Navigation

  44. Bayesian Methods – Sensor Models Histograms Panos Trahanias: Autonomous Robot Navigation

  45. PATH PLANNING Panos Trahanias: Autonomous Robot Navigation

  46. Bug Algorithms Bug1 Panos Trahanias: Autonomous Robot Navigation

  47. Bug Algorithms Bug1 Panos Trahanias: Autonomous Robot Navigation

  48. Bug Algorithms Bug2 Panos Trahanias: Autonomous Robot Navigation

  49. Bug Algorithms Bug2 Panos Trahanias: Autonomous Robot Navigation

  50. Bug Algorithms Bug2 Panos Trahanias: Autonomous Robot Navigation

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