Optimal Reconfiguration of a Parallel Robot for Forward Singularities Avoidance in Rehabilitation Therapies. A Comparison via Different Optimization Methods

[EN] This paper presents an efficient algorithm for the reconfiguration of a parallel kinematic manipulator with four degrees of freedom. The reconfiguration of the parallel manipulator is posed as a nonlinear optimization problem where the design variables correspond to the anchoring points of the...

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Bibliographic Details
Authors: Llopis-Albert, Carlos|||0000-0002-1349-2716, Mata Amela, Vicente|||0000-0003-2255-0567, Valero Chuliá, Francisco José, Pulloquinga-Zapata, José, Zamora-Ortiz, Pau, Escarabajal-Sánchez, Rafael José
Format: article
Publication Date:2020
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/172658
Online Access:https://riunet.upv.es/handle/10251/172658
Access Level:Open access
Keyword:Parallel robot
Rehabilitation
Reconfiguration
Optimization
Trajectory planning
Direct singularities
INGENIERIA MECANICA
INGENIERIA DE SISTEMAS Y AUTOMATICA
Description
Summary:[EN] This paper presents an efficient algorithm for the reconfiguration of a parallel kinematic manipulator with four degrees of freedom. The reconfiguration of the parallel manipulator is posed as a nonlinear optimization problem where the design variables correspond to the anchoring points of the limbs of the robot on the fixed platform. The penalty function minimizes the forces applied by the actuators during a specific trajectory. Some constraints are imposed to avoid forward singularities and guarantee the feasibility of the active generalized coordinates for a certain trajectory. The results are compared with different optimization approaches with the aim of avoiding getting trapped into a local minimum and undergoing forward singularities. The comparison covers evolutionary algorithms, heuristics optimizers, multistrategy algorithms, and gradient-based optimizers. The proposed methodology has been successfully tested on an actual parallel robot for different trajectories.

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