This paper proposes a method for task based design of modular serial robotic arms using evolutionary algorithms (EA). We introduce a 3D kinematics and a global optimization for both topology and configuration from task specifications. The search features revolute as well as prismatic joints and any number of DOF to build up a solution without using any design knowledge. A study of the evolution dynamics gives some keys to set evolution parameters that enable artificial evolution. An adapted algorithm dealing with the topology/configuration search tradeoff is proposed, descibed, and discussed. Illustrations of the algorithms results are given and conclusions are drawn from their analysis. Perspectives of this work are given, extending its reach to control and complex system design.

The problem of finding optimal values in complex parameter optimization problems has often been solved with success by evolutionary algorithms (EAs). In many cases, these algorithms are employed as black-box methods over imprecisely known domains. Such problems arise frequently in engineering design. The principal barrier to the general use of EAs for those problems is the huge number of function evaluations that is often required. This makes EAs an impractical approach when the function evaluation depends on numerically heavy design analysis tools, for example, finite elements methods. This paper presents the use of kriging interpolation as a function approximation method for the construction of an internal model of the fitness landscape. This model is intended to guide the search process with a reduced number of fitness function evaluations.

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.