Cable Route Planning in Complex Environments Using Constrained Sampling

by Ilknur Kabul, Russell Gayle, and Ming Lin.

We present a route planning algorithm for cable and wire layouts in complex environments.  Our algorithm precomputes a global roadmap of the environment by using a variant of the probabilistic roadmap method (PRM) and performs constrained sampling near the contact space. Given the initial and the final configurations, we compute an approximate path using the initial roadmap generated on the contact space.  We refine the approximate path by performing constrained sampling and use adaptive forward dynamics to compute a penetration-free path.  Our algorithm takes into account geometric constraints like non-penetration and physical constraints like multi-body dynamics and joint limits.  We highlight the performance of our planner on different scenarios of varying complexity.




Benckmark I: Cable route planning on the Bridge Model



Benckmark II: Cable route planning on the House Model



Benckmark III: Cable route planning on the Building Model



Benckmark IV: Cable route planning on the Car Model





Cable Route Planning in Complex Environments Using Constrained Sampling,

Ilknur Kabul, Russell Gayle, Ming Lin

2007 ACM Solid and Physical Modeling Symposium (SPM'2007), Short Paper

UNC Technical Report


Related Links

Adaptive Dynamics of Articulated Bodies

Practical Local Planning in the Contact Space

Constraint-Based Motion Planning of Deformable Robots

FlexiPlan - Path Planning for Deformable Robots in Complex Environments



CB #3175, Department of Computer Science
University of North Carolina
Chapel Hill, NC 27599-3175