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UID:calendar.13499.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260408T164651Z
CREATED:20180712T131459Z
DESCRIPTION:In robotics\, motion planning algorithms have traditionally aim
 ed at finding feasible\, collision-free paths for a mobile system. However
 \, beyond feasible solutions\, in many applications it is important to com
 pute good-quality paths with respect to a given cost criterion. When a cos
 t function is defined on the configuration space of the system\, motion pl
 anning becomes a pathfinding problem in a continuous cost-space. The cost 
 function may be related to clearance to obstacles\, to energy consumption\
 , and to many other different criteria. In computational structural biolog
 y\, 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 th
 is context is important since they correspond to the most probable conform
 ational transitions. We have developed a variant of the popular RRT algori
 thm\, called Transition-RRT (T-RRT)\, to compute good-quality paths in hig
 h dimensional continuous cost-spaces. The idea is to integrate a stochasti
 c 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 difficul
 ty of this transition test depending on the evolution of the exploration p
 rocess\, and which significantly contributes to the overall performance of
  the method. T-RRT is a simple and general algorithm that can take into ac
 count any type of continuous\, smooth cost function defined on the configu
 ration space. It has been successfully applied to diverse robot path-plann
 ing problems as well as structural biology problems. We have also develope
 d several variants and improvements of the basic T-RRT algorithm to solve 
 more efficiently particular classes of problems\, and to guarantee (asympt
 otic) convergence to the optimal solution in an any-time fashion.  Biosket
 ch Juan Cortés received the engineering degree in control and robotics fro
 m the Universidad de Zaragoza (Spain) in 2000. In 2003\, he received the P
 h.D. degree in automated systems/robotics from the Institut National Polyt
 echnique 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. App
 lications 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 num
 erous European and French national projects in all these areas. Between 20
 09 and 2015\, he was co-chair of the IEEE-RAS TC on Algorithms for Plannin
 g and Control of Robot Motion. He has participated in the organization of 
 several international workshops\, and he coordinates the winter schools Al
 gorithms in Structural Bioinformatics (AlgoSB) since 2012.
DTSTART;TZID=Europe/Paris:20180719T160000
DTEND;TZID=Europe/Paris:20180719T160000
LAST-MODIFIED:20210526T120258Z
LOCATION:B203
SUMMARY:Sampling-based algorithms for path-finding in continuous cost-space
 s: applications to robotics and structural biology - Juan Cortés
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/13499
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