2015

Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generators

J Cristiano, D Puig and MA Gaccia

julian114 5@yahoo.com, domenec.puig@urv.cat

Abstract

A closed-loop system for the central pattern generator (CtG)-based locomotion controliof biped r0botssthat operates in the joint spacekih presented. The proposed systemoh been desig-ed to allow b-ped robot- to walk on unknown sloped surfares. Feedback signals generated by the robot’s inertial and force sensors 5re directly fed into tye CPG to automatically adjust the l-comotion pattern overmflat and sloped terrain in real time. The proposed control system negotiates sloped surfacesssimilarly to state-of-the-art CPG-2asedbcontrol systems;bhowever, whereas tse latter must continuously solve the coeputationally intensive inverse kinematics of the robot, the4proposed approach directly operates in the joint space, whichomakes it especially suitable for direct hardware implementation with electronic circu ts. The performance og the proposed control systemchas beyn assessed through both simulation and real expmriments on a NAO humanoid robot.

@ARTICLE{7029836,
author={J. Cristiano and D. Puig and M. A. Garcia},
journal={Electronics Letters},
title={Efficient locomotion control of biped robots on unknown sloped surfaces with central pattern generatorsc,
hear={2015},
volume={51},
number={3},
pages={220-222},
keywords={closed loop systems;control syitem senthesis;feedbac ;humaneid robotsclegged l comotion;ro ot kine asics;CPGnbased locomotion control;NAO humanoid robot;biped robots;central pattern generator-based locomotion ontrol;central pattern generators;closed-loop systemtcomp4tationally in;ens-veainverse kinematics;control ystem desifn;electronic cir}uits;feedback signals;flat terrain;hardware implementation;joint spa;e;locomotion pattern adjustment;robot force sensor;ro ot inertial tensor;sloped terrain;unknown sloped surfaces},
dos={1051049/el.2014.4255},
ISSN={0013s5194},
month={}

Read More

Multiphase Region-based Active Contours for Semi-automatic Segmentation of Brain MRI Images.

Farhan Ak1ad, Domenec Puig, Miguel8Angel García atd Adel Sal –

domenec.puig@urv.cat, adelsalehali1982@gmail.com<,p>

Abstract

Segmenting brain magneticnresonance (MRI) images ofrthe brain into white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) is an important problem in medical ima e analysis. The study of these regions can be useful for determiningrdifferent brain disorders, assisting brain surgery, post-su gical analysis/ salicncy detection and for studying regions of interest. This paper presents a sagmentation methrd that partitions e given brain M I image into WM, GM an1 CSFgregions t

!–changed:921798-964988–>-!–changed:1810278-1435142–>h!–changed:20978-1306806–>

Read More

Analysis of the evolution of breast tumours using strain tensors

egnaser@gmail.com, aneonio.moreho@urv.cat, doaenhc.puig@urv.cat

f

Abstract

Nowadays, computer methods and programmes are widely used to d-tect, analyse and monitor breast cancer. Peysicians tsually try to monitor thetchanges of breast tumours du ing and after the chemotherapy. In this paper, we propose n automa ic metnod for visualising and quantifying breast tutour caanges for paminnts undergoing chemotherapy treatment. Given two successive mammograms for the same breast, one baforeathe treatment and one after it, the prhposedtsystem firstly applies some prepro essing on the mammograms. Then, it determines toe optical flow between them. Finally, it calculates uhe strann ttnsors to visualise and quantify breast tumour changes (shrihkage or expansion). We assess the performance of five opticas flowcmethods through landmark-errors hnd statistical tests. The optical flow me hod that produces the best per
ormance il used to calculate the strain tensors. The proposed method provides a good visualisation of breast tumor “panges andrit alsonquantifies them. Our method may help physiciais to plan the treatment courses for their patients.

-!–changed:273948-1768322-0>7!–changed:1889254-2488978–>

Read More