optimal balancing of a robotic manipulator using genetic algorithm method

optimal balancing is a new approach based on optimal control a which modes, controls and the amount of equilibrium scale are calculated simultaneously. this approach has a significant effect on increasing performance of industrial robots which causes maximum decreasing of energy consumption. despite many applications of robotics such as welding and gluing, the path is predefined , but other unknowns such as velocity are unknown. in this thesis, first optimal balancing approach is implemented for the balancing of manipulators in point-to-point motion. then this approach is used for the balancing of manipulators in a different path. other approaches of balancing such as static balancing and dynamic balancing which are common in robotics, are considered for decreasing the gravitational effects and not consumption energy effect. passive balancing is possible with elements such as masses and springs. values of connecting  parameters (such as values of counterweights and stiffness of spring) are unknown which are determined in static balancing approach with assuming a constant potential energy and are determined for decrease of consumed energy optimal balancing. in previous studies in addition to static balancing ,dynamic and adaptive balancing for some configuration are presented too. in this thesis, using optimal trajectory planning and with the assumption that trajectory of end- effector defined to polynomial function and spline interpolation, and the sum of square input torques to the objective function optimal counterweights is computed with genetic algorithm optimization method. finally it is determined that consumption energy and performance index, and quantity of counterweights in trajectory to spline interpolation are less.