TY - JOUR
T1 - Evaluating concurrent design approaches for a Delta parallel manipulator
AU - Botello-Aceves, Salvador
AU - Valdez, S. Ivvan
AU - Becerra, Héctor M.
AU - Hernandez, Eusebio
N1 - Publisher Copyright:
© Cambridge University Press 2018.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - This paper addresses the problem of optimal mechanisms design, for the geometric structure and control parameters of mechanisms with complex kinematics, which is one of the most intricate problems in contemporary robot modeling. The problem is stated by means of task requirements and performance constraints, which are specified in terms of the end-effector's position and orientation to accomplish the task. Usually, this problem does not fulfill the characteristics needed to use gradient-based optimization algorithms. In order to circumvent this issue, we introduce case studies of optimization models using evolutionary algorithms (EAs), which deal with the concurrent optimization of both: structure and control parameters. We define and review several optimization models based on the workspace, task and dexterity requirements, such that they guarantee an adequate performance under a set of operating and joint constraints, for a Delta parallel manipulator. Then, we apply several methodologies that can approximate optimal designs. Additionally, we compare the EAs with a quasi-Newton method (the BFGS), in order to show that the last kind of methods is not capable of solving the problem if the initial point is not very close to a local optimum. The results provide directions about the best state-of-the-art EA for addressing different design problems.
AB - This paper addresses the problem of optimal mechanisms design, for the geometric structure and control parameters of mechanisms with complex kinematics, which is one of the most intricate problems in contemporary robot modeling. The problem is stated by means of task requirements and performance constraints, which are specified in terms of the end-effector's position and orientation to accomplish the task. Usually, this problem does not fulfill the characteristics needed to use gradient-based optimization algorithms. In order to circumvent this issue, we introduce case studies of optimization models using evolutionary algorithms (EAs), which deal with the concurrent optimization of both: structure and control parameters. We define and review several optimization models based on the workspace, task and dexterity requirements, such that they guarantee an adequate performance under a set of operating and joint constraints, for a Delta parallel manipulator. Then, we apply several methodologies that can approximate optimal designs. Additionally, we compare the EAs with a quasi-Newton method (the BFGS), in order to show that the last kind of methods is not capable of solving the problem if the initial point is not very close to a local optimum. The results provide directions about the best state-of-the-art EA for addressing different design problems.
KW - BUMDA
KW - CMA-ES
KW - Concurrent design
KW - Delta robot
KW - Design
KW - Dimensional synthesis
KW - Evolutionary algorithms
KW - GA
KW - Mechanisms with complex kinematics
UR - http://www.scopus.com/inward/record.url?scp=85043316819&partnerID=8YFLogxK
U2 - 10.1017/S0263574717000674
DO - 10.1017/S0263574717000674
M3 - Artículo
SN - 0263-5747
VL - 36
SP - 697
EP - 714
JO - Robotica
JF - Robotica
IS - 5
ER -