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Juan Cortés
presents
Sampling-based algorithms for path-finding in continuous cost-spaces:
 
On 2018-03-06 10:00 at E112
 
Sampling-based algorithms for path-finding in continuous cost-spaces: 
applications to robotics and structural biology

Summary:
In robotics, motion planning algorithms have traditionally aimed at finding
feasible, collision-free paths for a mobile system. However, beyond feasible
solutions, in many applications it is important to compute good-quality paths
with respect to a given cost criterion. When a cost function is defined on the
configuration space of the system, motion planning becomes a pathfinding
problem
in a continuous cost-space. The cost function associated with robot
configurations may be defined from the distance to obstacles in order to find
high-clearance solution paths. It may also be related to controllability, to
energy consumption, or to many other different criteria. In computational
structural biology, where robotics-inspirited algorithms are applied to
simulate
molecular motions, the cost function is usually
defined by the potential energy or the free energy of the molecular system.
Computing low energy paths in this context is important since they correspond
to
the most probable conformational transitions.
We have developed a variant of the popular RRT algorithm, called Transition-RRT
(T-RRT), to compute good-quality paths in high dimensional continuous
cost-spaces. The idea is to integrate a stochastic state-transition test,
similarly to the Metropolis Monte Carlo
method, which makes the exploration get focused on low-cost regions of the
space. The algorithm involves a self-tuning mechanism that controls the
difficulty of this transition test depending on the evolution of the
exploration
process, and which significantly contributes
to the overall performance of the method. T-RRT is a simple and general
algorithm that can take into account any type of continuous, smooth cost
function defined on the configuration space. It has been successfully applied
to
diverse robot path-planning problems as well as structural biology problems. We
have also developed several variants and improvements of the basic T-RRT
algorithm to solve more efficiently particular classes of problems, and to
guarantee (asymptotic) convergence to the optimal solution in an any-time
fashion.

Biography:
Juan Cortés received the engineering degree in control and robotics from the
Universidad de Zaragoza (Spain) in 2000. In 2003, he received the Ph.D. degree
in automated systems/robotics from the Institut National Polytechnique de
Toulouse (France). From 2004, he is CNRS researcher at LAAS (Toulouse, France).
His research interest is focused on the development of algorithms for computing
and analyzing the motion of complex systems. Applications of these algorithms
go
beyond robotics. Indeed, he is strongly involved in interdisciplinary research
in the areas of structural biology, biotechnology and materials science. Juan
Cortés has participated in numerous European and French national projects in
all these areas. Between 2009 and 2015, he was co-chair of the IEEE-RAS TC on
Algorithms for Planning and Control of Robot Motion. He has participated in the
organization of several international workshops, and he coordinates the winter
schools Algorithms in Structural Bioinformatics (AlgoSB) since 2012.
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