Autonomous Robotic Arm Control Using Hybrid Kinematic Optimization

Abstract

The autonomous robotic manipulators have become inevitable in the contemporary industrial automation, medical robotics, space exploration, and service robots. But it is an inherent challenge to have accurate, active and strong control of robotic arms under dynamic and uncertain conditions. Conventional control techniques utilizing only forward or inverse kinematics have drawbacks of singularities, local minima, sluggish convergence and lack of computational efficiency. In order to solve these problems, the current paper will cover a new autonomous robotic arm control system (ARCs) by relying on a Hybrid Kinematic Optimization(HKO) approach that combines analytical inverse kinematics, numerical optimization, intelligent constraint management. The suggested framework will integrate classical DenavitHartenberg (D-H) kinematic modeling with the use of the gradient-based and evolutionary optimization to produce the optimal joint trajectories in real-time. There is the introduction of a hybrid cost function that involves position accuracy, orientation error, joint smoothness, and energy efficiency. Collision avoidance and workspace constraints are also factored in the control architecture to be used to ensure safe and reliable operation. The hybrid optimizer is a dynamical algorithm that changes between fast analytical solvers and the global numerical optimizers based on the complexity of the task and the environmental conditions. Experiments involving simulation experiments on a 6-DOF model of industrial robotic arm on different task settings, such as pick-and-place settings, obstacle avoidance, and tracking in a trajectory are carried out. Convergence rate, tracking accuracy, joint torque efficiency and computational load are among the performance metrics considered and compared to the more traditional inverse kinematics and pure optimization-based methods. Findings indicate that the suggested hybrid structure attains a maximum speed of convergence is 35 percent, trajectory error is 28 percent and energy use is 22 percent. The Hybrid Kinematic Optimization framework presented provides a robust, scalable and intelligent framework applicable to solve the needs of next generation autonomous robotic manipulators to work within dynamic environments.