Randomized Optimal Design of Parallel Manipulators
ABSTRACT This work intends to deal with the optimal kinematic synthesis problem of parallel manipulators under a unified framework. Observing that regular (e.g., hyper-rectangular) workspaces are desirable for most machines, we propose the concept of effective regular workspace, which reflects simultaneously requirements on the workspace shape and quality. The effectiveness of a workspace is characterized by the dexterity of the mechanism over every point in the workspace. Other performance indices, such as manipulability and stiffness, provide alternatives of dexterity characterization of workspace effectiveness. An optimal design problem, including constraints on actuated/passive joint limits and link interference, is then formulated to find the manipulator geometry that maximizes the effective regular workspace. This problem is a constrained nonlinear optimization problem without explicitly analytical expression. Traditional gradient based approaches may have difficulty in searching the global optimum. The controlled random search technique, as reported robust and reliable, is used to obtain an numerical solution. The design procedure is demonstrated through examples of a Delta robot and a Gough-Stewart platform.
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ABSTRACT: The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized simultaneously to improve dexterity over a required workspace, since single optimization of an objective function may not ensure an acceptable design. A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (CENSGA) is used to find the Pareto front.The Open Mechanical Engineering Journal 12/2010; 4:37-47.
Article: Comparison of Planar Parallel Manipulator Architectures based on a Multi-objective Design Optimization Approach[show abstract] [hide abstract]
ABSTRACT: This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-RPR, 3-RPR and 3-RPR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.12/2010;
Conference Proceeding: CAD-enhanced workspace optimization for parallel manipulators: A case study[show abstract] [hide abstract]
ABSTRACT: The challenges of workspace determination of parallel manipulators (PMs) arise principally from the lack of analytical solutions of the forward kinematics. The inverse-position kinematics based approach for determining workspace tends to be inefficient, time consuming and unsophisticated. In this paper, we present a geometry-based method for accurate and computationally effective calculation of the workspace of a constrained parallel manipulator. We illustrate how boolean geometric operations can simplify the process of finding the workspace and optimizing the designs. Comparative performance studies, in terms of accuracy and computational performance, are performed to benchmark the approach against more conventional methods. Finally, we examine ways to further automate the process using a CAD package.Automation Science and Engineering (CASE), 2010 IEEE Conference on; 09/2010