Optimal trajectory planning by Big Bang-Big Crunch algorithm

Path planning is an interesting topic which is affected by lots of variables, as: time, energy, torque and stability. In this study, a new method based on Big Bang-Big Crunch algorithm is proposed to find optimum values of the parameters of a path and a cost function in order to minimize applied torque and tracking error. For this purpose the mathematical model of the manipulator is derived with mainly used methods, Denavit-Hartenberg, Jacobian and Euler-Lagrange methods. By using classical robot modeling methods, Big Bang-Big Crunch algorithm searched for the optimum trajectory and found the optimum value of the cost function.