Roland Geraerts and Mark Overmars
ICRA 2007
The Corridor Map Method:Real-Time High-Quality Path Planning
Previous work
• Potential field planners – Flexible– Slow / local minima
• Probabilistic Roadmap Methods– Fast – Ugly paths– Output: fixed paths in response to a query
• Predictable motions • Lacks flexibility when environment changes
Our planner
• Requirements– High-quality paths– Flexible– Extremely fast
• Current limitations– The robot is modeled by a disc– Experiments with only 2D problems
The Corridor Map Method
• Construction phase (off-line)– Create a system of collision-free corridors for
the static obstacles
Graph Corridor map: graph + clearance
The Corridor Map Method
• Query phase (on-line)– Extract corridor for given start and goal– Extract path by following attraction point
Corridor: backbone path + clearance
Query Path
The Corridor Map Method
• Attraction point α(x)– Robot’s location: x– Robot’s goal: g– Radius circle: r– Euclidean distance: d
• Path is obtained by integration over time while updating the velocity, position, and attraction point of the robot
• For other behavior: locally adjust robot’s path by adding forces
α(x)
x
g
r
d
Avoiding obstacles
• Adding forces– For each obstacle,
add repulsive force to the robot
• Creating a sub-corridor– For each obstacle, move
backbone path locally and recompute clearance info
Creating shorter paths
• Attraction point α(x) corresponds to point B[t] on the backbone path
• Add additional valid attraction point α(x, Δt), corresponding to point B[t + Δt]
• Valid means: x can see point B[t + Δt]
α(x, 0.00) α(x, 0.05) α(x, 0.10) α(x, 0.25)
Experimental setup
• Single path planning system• Created in Visual C++, Windows XP• 2.66 GHz P4 processor, 1 GB memory• Each experiment was run 100 times• Statistics: running time of query phase, CPU load• Input graphs created using
– “Creating High-quality Roadmaps for Motion Planning in Virtual Environments“- IROS 2006
– Environments were discretized: 100x100 cells
Experimental setup
• Maze • Field
1.6 seconds 20 seconds
Experiments – Smooth paths
• Maze
Query time: 2.41 ms
CPU load: 0.026%
• Field
Query time: 0.84 ms
CPU load: 0.029%
Experiments – Obstacles
• Maze: adding forces
Query time: 7.0—9.0 ms
CPU load: 0.05—0.06%
• Maze: sub-corridor
Query time: 3.0—13.6 ms
CPU load: 0.025—0.10%
Experiments – Obstacles
• Maze
Experiments – Obstacles
• Field: adding forces
Query time: 2.0—2.3 ms
CPU load: 0.05—0.05%
• Field: sub-corridor
Query time: 1.0—7.0 ms
CPU load: 0.03—0.16%
Experiments – Obstacles
• Field
Experiments – Short paths
• Maze: Δt = 0
Query time: 2.41 ms
CPU load: 0.026%
• Maze: Δt = 0.2
Query time: 9.64 ms
CPU load: 0.104%
Experiments – Short paths
• Maze
Experiments – Short paths
• Field: Δt = 0
Query time: 0.84 ms
CPU load: 0.029%
• Field: Δt = 0.2
Query time: 3.36 ms
CPU load: 0.116%
Experiments – Short paths
• Field
Conclusions
• The CMM produces high-quality paths– Natural paths: smooth, short / large clearance
• The CMM is flexible– Paths are locally adjustable
• The CMM is fast– CPU load < 0.1%
Future work
• Extend experimentswith 2½D / 3D problems
• Study applications– Planning of a group– Steering a camera– Alternative routes– Tactical planning