This thesis presents practical methods for planning and control to improve the motion performance of industrial robots. Particular attention is given to the commercial six degrees-of-freedom articulated robot with a low-cost generic controller. A comparative study of motion control methods demonstrated that both smooth trajectory planning and filtering techniques, when combined with a traditional Proportional-Derivative control, are limited in achievable performance due to reduced accelerations (smooth trajectory) or large path-distortions (filtering technique). Instead, faster and more accurate motion is achieved with a low-order trajectory, namely, trapezoidal velocity profile, with feedforward control design based on an elastic model. The key component that makes the latter approach more appealing is the delay-free dynamic input shaper embedded in the feedforward control. Following the results from the comparative study, two innovations are proposed to satisfy the path-invariant and time-optimal motion. First, an online time-optimal trapezoidal velocity profile planned along multiple path segments is presented. The trajectory can be planned for arbitrary boundary conditions and path curvatures with only four system-dynamics computations per path segment. Next, a novel control method based on the flexible joint dynamic model is proposed to achieve high tracking performance for the proposed trajectory. The proposed nonlinear multivariable control can place the closed-loop poles arbitrarily with only position and velocity feedback. Real-world experiments with commercial industrial robots are carried out to validate the presented methods.