Link Mass Optimization Using Genetic Algorithms for Industrial Robot Manipulators

Obtaining optimum energy performance is the primary design concern of any mechanical system, such as ground vehicles, gear trains, high speed electromechanical devices and especially industrial robot manipulators. The optimum energy performance of an industrial robot manipulator based on the minimum energy consumption in its joints is required for developing of optimum control algorithms (Delingette et al., 1992; Garg & Ruengcharungpong, 1992; Hirakawa & Kawamura, 1996; Lui & Wang, 2004). The minimization of individual joint torques produces the optimum energy performance of the robot manipulators. Optimum energy performance can be obtained to optimize link masses of the industrial robot manipulator. Having optimum mass and minimum joint torques are the ways of improving the energy efficiency in robot manipulators. The inverse of inertia matrix can be used locally minimizing the joint torques (Nedungadi & Kazerouinian, 1989). This approach is similar to the global kinetic energy minimization. Several optimization techniques such as genetic algorithms (Painton & Campbell, 1995; Chen & Zalzasa, 1997; Choi et al., 1999; Pires & Machado, 1999; Garg & Kumar, 2002; Kucuk & Bingul, 2006; Qudeiri et al., 2007), neural network (Sexton & Gupta, 2000; Tang & Wang, 2002) and minimax algorithms (Pin & Culioli, 1992; Stocco et al., 1998) have been studied in robotics literature. Genetic algorithms (GAs) are superior to other optimization techniques such that genetic algorithms search over the entire population instead of a single point, use objective function instead of derivatives, deals with parameter coding instead of parameters themselves. GA has recently found increasing use in several engineering applications such as machine learning, pattern recognition and robot motion planning. It is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. This provides a robust search procedure for solving difficult problems. In this work, GA is applied to optimize the link masses of a three link robot manipulator to obtain minimum energy. Rest of the Chapter is composed of the following sections. In Section II, genetic algorithms are explained in a detailed manner. Dynamic equations and the trajectory generation of robot manipulators are presented in Section III and Section IV, respectively. Problem definition and formulation is described in Section V. In the following Section, the rigid body dynamics of a cylindrical robot manipulator is given as example. Finally, the contribution of this study is presented in Section VII.