Hybrid architecture for autonomous indoor navigation

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# Hybrid architecture for autonomous indoor navigation - PowerPoint PPT Presentation

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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Presentation Transcript
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!