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A multi-objective optimization method is proposed for optimal motor selection, control, and planning for a point-topoint trajectory of a robot manipulator, where three objective functions are proposed to be minimized: the total weight of actuators, the execution time and velocity transitions between planned points, and the tracking error of the task. A concurrent approach is proposed where the powertrain dynamics of the robot is taken into account, that is, motor, gearbox, and load at each actuated joint. To solve the concurrent optimization problem, a genetic algorithm is used, where a representative set of non-dominated solutions form the Pareto-front. The method is tested for a 3-degree-offreedom manipulator by selecting a particular solution.

Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, commercial, and social reasons. Thus, research of manufacturing, designing, and signal processing may lead up to a low-cost affordable prosthesis. This manuscript presents a low-cost design proposal for an electromyographic electronic system, which is characterized by a neural network based process. Moreover, a hand-type prosthesis is presented and controlled by using the processed electromyographic signals for a required particular use. For this purpose, the user performs several movements by using the healthy-hand to get some electromyographic signals. After that, the obtained signals are processed in a neural network based controller. Once an usable behavior is obtained, an exact replica of controlled motions are adapted for the other hand by using the designed prosthesis. The characterization process of bioelectrical signals was performed by training twenty characteristics obtained from the original raw signal in contrast with other papers in which seven characteristics have been tested on average. The proposed model reached a 95.2% computer test accuracy and 93% accuracy in a real environment experiment. The platform was tested via online and offline, where the best response was obtained in the online execution time.

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