Motion-Force Control for Redundant Robots With Orientation Maintenance

In the realm of industrial automation, the control of redundant robots is essential, particularly in precision manufacturing. Traditional dynamics-based methods often face challenges in handling the complexity of redundant degrees of freedom in robots. This paper introduces an innovative approach to motion-force control utilizing joint velocity commands based on kinematic analysis, representing a shift from traditional dynamics-based methods. The problem of motion and force control is organized into a constrained quadratic programming(QP) scheme by integrating desired forces, predefined trajectories, and orientation maintenance. A recurrent neural network (RNN) is presented to achieve redundant robot kinematic control. The proposed scheme achieves accurate hybrid control of force and position, which is confirmed by simulation results. This approach has the potential to enhance the efficiency and precision of industrial automation systems.