1. Planning

2. Cognition Where am I going ? How do I get there ?
(global) Path Planning
(local) Obstacle Avoidance

3. Configuration Space

4. Path Planning Overview
Assumptions
There exists a good enough map of the environment for navigation
Topological map: graph-like structure (nodes/edges)
Grid map
Representation of environments
Road map
Identify a set of routes within the free space
Cell decomposition
Discriminate between free and occupied cells
Potential field
Impose a mathematical function over the space

5. Visibility Graph

6. Visibility Graph V-graph path Shortest path Simple implementation
Number of nodes and edges increase with the number of obstacle polygons
Take the robot as close as possible to obstacle

7. Voronoi Diagram
Not shortest path
Safe path

8. Voronoi Diagram

9. Cell-Decomposition Exact cell decomposition
Number and size of cells depends on the density and complexity of objects
Cell
Connectivity
graph

10. Cell-Decomposition Approximate cell decomposition
Grid-based representation
Low computational complexity of path planning algorithm
(ex) wave-front algorithm
Large memory & loss of object shape
Adaptive cell decomposition

11. Path Planning Algorithm for Road-Map & Cell Decomposition

12. Path Planning: Wavefront Expansion

13. Path Planning: Breadth-First Search

14. Path Planning: Depth-First Search

15. Path Planning: A*

16. Potential Field

17. Potential Field

18. Potential Field

19. Path Planning: Potential Field
Local minimum problem

20. Obstacle Avoidance Local path planning
Changing robot’s trajectory as informed by its sensors during robot motion

21. Obstacle Avoidance: Bug 1

22. Obstacle Avoidance: Bug 1
Exhaustive search to find leave point

23. Obstacle Avoidance: Bug2

24. Obstacle Avoidance: Bug2
Greedy search to find leave point

25. Obstacle Avoidance: Tangent Bug

26. Obstacle Avoidance: Tangent Bug

27. Obstacle Avoidance: Tangent Bug

28. Obstacle Avoidance: Tangent Bug

29. Obstacle Avoidance: Tangent Bug

30. Obstacle Avoidance: Tangent Bug

31. Obstacle Avoidance: Vector field histogram(VFH)
Polar histogram

32. Obstacle Avoidance: VFH+
Considering kinematic limitations
(ex) Turning radius of vehicle
Masked polar histogram

33. Obstacle Avoidance: Bubble Band Concept

34. Obstacle Avoidance: Curvature velocity approach
Basic curvature velocity method (CVM)
Assumption
robot only travels along arcs with curvature c = w/v
Adding physical constraints from the robot and the environment to a velocity space
Robot’s kinematic and dynamic constraints
-vmax < v < vmax , -ωmax < ω < ωmax
Constraints from obstacle blocking certain v and ω
Obstacles are approximated by circular objects
New velocity (v and ω) is made by an object function
tv: translational velocity
rv: rotational velocity

35. Obstacle Avoidance: Dynamic window approach
Local dynamic window approach

36. Obstacle Avoidance: Dynamic window approach

37. Obstacle Avoidance: ASL approach

38. Obstacle Avoidance: ASL approach

39. Obstacle Avoidance: Overview

40. Obstacle Avoidance: Overview

41. Obstacle Avoidance: Overview