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

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

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

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

11. A* and VFH

12. Global planning: How to apply A*

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

14. 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 A star

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

16. 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 A star

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!