Personal tools
You are here: Home CG seminar Fall 2017 Sampling-Based Bottleneck Pathfinding with Applications to Fréchet Matching
« May 2017 »
May
SuMoTuWeThFrSa
123456
78910111213
14151617181920
21222324252627
28293031
Log in


Forgot your password?
 

Sampling-Based Bottleneck Pathfinding with Applications to Fréchet Matching

Wednesday, December 14th, 2016, 16:10

Schreiber 309

underline

Sampling-Based Bottleneck Pathfinding with Applications to Fréchet Matching

Kiril Solovey, TAU

Abstract:

We describe a general probabilistic framework to address a variety of
Fréchet-distance optimization problems. Specifically, we are
interested in finding minimal bottleneck-paths in $d$-dimensional
Euclidean space between given start and goal points, namely paths that
minimize the maximal value over a continuous cost map. We present an
efficient and simple sampling-based framework for this problem, which
is inspired by, and draws ideas from, techniques for robot motion
planning.  

On the theoretical side, we study the asymptotic properties of our
method and also consider the special setting where the computed
trajectories must be monotone in all coordinates. This constraint
arises in cases where the underlying problem involves the coordination
of multiple agents that are restricted to forward motions along
predefined paths, e.g., the strong-Frechet case.

The talk is based on the following two papers:

[1] Kiril Solovey, Dan Halperin: Sampling-Based Bottleneck Pathfinding
with Applications to Fréchet Matching. ESA 2016: 76:1-76:16

[2] Kiril Solovey, Dan Halperin: Efficient sampling-based bottleneck
pathfinding over cost maps. CoRR abs/1608.00261 (2016)
Document Actions