Optimal control-based quantum genetic algorithm for a six jointed articulated robotic arm

This paper explores the use of a quantum genetic algorithm (QGA) for finding the best control considering a calculated path for a six-jointed robotic arm. Classical genetic algorithms (GAs) are typically used to solve optimization problems in robot manipulators, however, QGAs bring a consistent advantage in terms of solution quality. In fact, this study uses a QGA simulated on classical hardware to create optimal control law based on a fifth-order polynomial path, aiming to minimize the tracking error of the position. Eventually, it compares positional error and energy consumption used by actuators through its cost function with to the classical methods. The simulation demonstrates that the QGA tends to be better than real-coded and binary-coded genetic algorithms (respectively RCGA and BCGA), especially when it comes to tracking accuracy, energy, and maintaining stability in noisy conditions. This indicates its potential use in real-time robotics applications by exploring quantum algorithms' practical benefits over traditional optimization methods for complex tasks with multiple dimensions in robot systems control.