1. A Comparative Study of Probabilistic Roadmap Planners Roland Geraerts Mark Overmars

2. Introduction Probabilistic roadmaps Scenes Collision checking Sampling Node adding Conclusions

3. Probabilistic Roadmaps free spaceforbidden space

4. Probabilistic Roadmaps sample

5. Probabilistic Roadmaps startgoal

6. Important Choices Sampling technique Neighbor selections (node adding) Notion of distance Local method Straight-line motion (in C-space) Collision checker

7. Experimental Setup Environment Single system SAMPLE Same computer Pentium IV 2.4GHz, 1GB memory Same test scenes Corridor, rooms, clutter, hole, house Preprocessing method but single query No cycles Average time over 20 runs

8. Scenes Corridor Simple scene L-shaped robot Scene forces rotation Rooms Free space, 2 corridors Table robot Density is non-uniform

9. Scenes Clutter 500 tetrahedra L-shaped robot Many paths possible Hole Narrow corridor 4 legs robot A few paths possible

10. Scenes House 1600 polygons Block-shaped robot Small stepsize

11. Collision Checking Incremental versus binary

12. Collision Checking Incremental versus binary

13. Collision Checking Incremental versus binary

14. Collision Checking Incremental versus binary

15. Collision Checking Incremental versus binary IncrementalBinary Corridor0.50.2 Rooms0.80.3 Clutter8.74.0 Hole44.343.3 House380.0207.4

16. Collision Checking Line checking versus no line checking No lineLine check Corridor0.2 Rooms0.3 Clutter4.04.3 Hole43.344.5 House207.4208.1

17. Sampling Random Grid

18. Sampling Halton Cell-based

19. Sampling Basic sampling strategy randomgridhaltoncell- based Corridor0.41.40.20.4 Rooms0.80.60.30.7 Clutter2.77.34.03.1 Hole33.926.243.342.3 House210.0262.5207.4225.9

20. Sampling Halton points Deterministicrandom Corridor0.20.3 Rooms0.30.2 Clutter4.02.2 Hole43.315.8 House207.4152.9

21. Sampling Gaussian Obstacle-based

22. Sampling Sampling around obstacles gaussianobstacleobstacle*halton Corridor0.40.5 0.3 Rooms0.40.70.50.2 Clutter5.63.77.92.2 Hole3.18.02.115.8 House268.0197.1209.4152.9

23. Node Adding Nearest-n Connect to nearest k nodes in graph Component Connect to nearest nodes in connected component Component-n Idem, but connect to at most n nodes in each cc Visibility Connect to useful nodes

24. Node Adding Node adding strategy nearest-ncompcomp-nvisibility Corridor0.20.80.51.5 Rooms0.30.50.72.3 Clutter4.04.43.47.6 Hole43.3>12031.920.9 House207.4279.6189.1>600

25. Conclusions Collision checking Binary works better than incremental Sampling Halton Previous claims couldn’t be confirmed Adding randomness gave better performance Maximal difference performance is not that high

26. Conclusions Node adding Visibility sampling didn’t perform as well as expected Component-n seems to work best A careful choice of techniques is important It isn’t necessarily true that a combination of good techniques is good

27. Future Work Incorporate other techniques Medial axis Edge connections Sampling in difficult regions Hybrid techniques Learning techniques Other local methods Other motion planning problems Non-holonomic Articulated