Control of a 7-DOF robotic manipulator based on multi-objective optimization (MOO)
Our platform uses multi-objective optimization (MOO) methods to control a seven-degree-of-freedom (7DOF) robotic manipulator. This approach ensures precise end-effector positioning by balancing multiple objectives, including cost minimization and transition speed maximization.
With MOO, users achieve high efficiency and optimal control, performing complex tasks with increased accuracy and performance.
In addition, our control system incorporates dynamic weighting of criteria that adapts to real-time feedback, ensuring smooth motion even under uncertainty. A Pareto-based optimization algorithm identifies trade-offs between accuracy, energy consumption, and joint torque constraints. This enables safe and stable operation across various industrial and research scenarios. As a result, the system delivers robust intelligent control with improved task accuracy and reduced computational load