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Details

  • Name

    Flora Rocha Ferreira
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st September 2017
Publications

2018

Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance

Authors
Faria, C; Ferreira, F; Erlhagen, W; Monteiro, S; Bicho, E;

Publication
Mechanism and Machine Theory

Abstract
This paper presents a novel analytic method to uniquely solve inverse kinematics of 7 degrees-of-freedom manipulators while avoiding joint limits and singularities. Two auxiliary parameters are introduced to deal with the self-motion manifolds: the global configuration (GC), which specifies the branch of inverse kinematics solutions; and the arm angle (?) that parametrizes the elbow redundancy within the specified branch. The relations between the joint angles and the arm angle are derived, in order to map the joint limits and singularities to arm angle values. Then, intervals of feasible arm angles for the specified target pose and global configuration are determined, taking joint limits and singularities into account. A simple metric is proposed to compute the elbow position according to the feasible intervals. When the arm angle is determined, the joint angles can be uniquely calculated from the position-based inverse kinematics algorithm. The presented method does not exhibit the disadvantages inherent to the use of the Jacobian matrix and can be implemented in real-time control systems. This novel algorithm is the first position-based inverse kinematics algorithm to solve both global and local manifolds, using a redundancy resolution strategy to avoid singularities and joint limits. © 2017 Elsevier Ltd

2018

Towards temporal cognition for robots: A neurodynamics approach

Authors
Wojtak, W; Ferreira, F; Louro, L; Bicho, E; Erlhagen, W;

Publication
2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

Abstract

2018

Foot clearance pattern: a distinctive gait variable in vascular Parkinson's disease

Authors
Gago, MF; Ferreira, F; Carvalho, C; Mollaei, N; Bicho, E; Rodrigues, L; Sousa, N; Gama, J; Ferreira, C;

Publication
EUROPEAN JOURNAL OF NEUROLOGY

Abstract

2017

Gait analysis as a complementary tool in the levodopa dose decision in vascular Parkinson's disease

Authors
Gago, M; Ferreira, F; Mollaei, N; Rodrigues, M; Sousa, N; Bicho, E; Rodrigues, P;

Publication
MOVEMENT DISORDERS

Abstract

2017

Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks

Authors
Aguiar, MAD; Dias, APS; Ferreira, F;

Publication
CHAOS

Abstract
We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.