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Hybrid architecture for autonomous indoor navigation. Serge Belinski Cyril Roussillon. Georgia Institute of Technology CS 7630 – Autonomous Robotics Spring 2008. Problem Statement. Autonomous navigation in a building using an a priori map and sonar sensors. Global planning: A star.

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Hybrid architecture for autonomous indoor navigation


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    1. Hybrid architecture for autonomous indoor navigation Serge Belinski Cyril Roussillon Georgia Institute of Technology CS 7630 – Autonomous Robotics Spring 2008

    2. Problem Statement • Autonomous navigation in a building • using an a priori map • and sonar sensors

    3. Global planning:A star

    4. Algorithm • Graph best-first optimal path search • Heuristic = estimation of distance • A* optimal  heuristic admissible (lower bound)‏ • e.g. euclidian distance • cost(S  G | A) ≥ dist(S  A) + heur(A  G)‏ • Explores the most promising partial path

    5. Algorithm • Initialization: • Current node = start node • Closed list = start node (nodes already considered)‏ • Open list = empty (nodes to consider, exploration front)‏ • Nth step: • Find neighbors of current node (no obstacles or closed list)‏ • For every neighbor: • If goal →end: path = parents • If in open list →update if better (cost and parent) • Else add in open list (cost and parent)‏ • Find the best candidate node in open list: • If open list empty →end: no solution • Else move from open list to closed list set as current node

    6. A* returns

    7. Local obstacle avoidance:Vector Field Histogram

    8. Vector Field Histogram Histogram Grid • Inspired by certainty grids • increases one cell per reading • accumulation of readings creates certainty values

    9. Vector Field Histogram Polar Histogram Restrained active window Angular obstacle density “Thresholded”

    10. Vector Field Histogram Adaptations • Maximum value for histogram • if robot stays still • Decrease histogram values • → dynamic obstacles

    11. A* and VFH

    12. Global planning:How to apply A*

    13. A star Modelization problems • Grid map → modelized as a graph • Usual way → immediate neighbors ........... • Problems: • Slow and memory-consuming for large grids • Gives low-level pathWant high-level path • Interpolation of discrete path does not give optimal continuous path ....................

    14. A star Solution proposed • Neighbors = connectable by a straight line without obstacle …………………. • Problems: • Graph of huge degree • Vicinity test pretty slow • Solutions: • Reduce the number of vertices • Precompute the graph

    15. A star Candidate intermediary points • Cells tangent to obstacles in convex parts • connect any pair of grid points • with a shortest path

    16. A star Characterization • Using a simple mask: • And the policy: • no purple cell obstacle • exactly one blue cell obstacle • at most one green “side” contains more than one obstacle cell

    17. A* and VFH

    18. Testing

    19. A* Navigation points = blue points Dilation of obstacle map for embodiment

    20. Demonstration Small environment of two rooms simulated With unknown static and dynamic obstacles [Video]

    21. Improvements More and faster sonar → faster robot Better localization than dead-reckoning for large maps Instability in the choice of the valley in VFH Parameters tuning still improvable

    22. Thank you!