A Comparative Study of Probabilistic Roadmap Planners Roland Geraerts Mark Overmars

Introduction Probabilistic roadmaps Scenes Collision checking Sampling Node adding Conclusions

Probabilistic Roadmaps free spaceforbidden space

Probabilistic Roadmaps sample

Probabilistic Roadmaps startgoal

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

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

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

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

Scenes House 1600 polygons Block-shaped robot Small stepsize

Collision Checking Incremental versus binary

Collision Checking Incremental versus binary

Collision Checking Incremental versus binary

Collision Checking Incremental versus binary

Collision Checking Incremental versus binary IncrementalBinary Corridor Rooms Clutter Hole House

Collision Checking Line checking versus no line checking No lineLine check Corridor0.2 Rooms0.3 Clutter Hole House

Sampling Random Grid

Sampling Halton Cell-based

Sampling Basic sampling strategy randomgridhaltoncell- based Corridor Rooms Clutter Hole House

Sampling Halton points Deterministicrandom Corridor Rooms Clutter Hole House

Sampling Gaussian Obstacle-based

Sampling Sampling around obstacles gaussianobstacleobstacle*halton Corridor Rooms Clutter Hole House

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

Node Adding Node adding strategy nearest-ncompcomp-nvisibility Corridor Rooms Clutter Hole43.3> House >600

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

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

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