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