Minimizing constraint forces and moments of manipulators using teaching–learning-based optimization and octahedron point mass model

Abstract

The octahedron seven point mass model of equimomental point masses and optimization technique “teaching–learning-based optimization” is presented to minimize constraint forces and moments at joints of an industrial manipulator. The octahedron point mass model configuration presented offers positive values for equimomental point masses to facilitate link shape formation. Equations are derived to compute point masses and their locations for the rigid links of an industrial manipulator. The flow chart of the teaching–learning-based optimization applicable in solving the manipulators problem is presented and used. The constraint moments at heavily loaded joints are reduced significantly. Moreover, the maximum values of driving torques are also reduced at joints. It is observed that teaching–learning-based optimization gives better results with less computational effort vis-à-vis genetic algorithm for the manipulator optimization problem formulated. The teaching–learning-based optimization algorithm introduced first time for manipulator balancing as optimization solver.