In this paper, a practical dynamic path tracking (DPT) scheme for industrial robots is presented. The DPT scheme is a position-based visual servoing to realize three-dimensional dynamic path tracking by correcting the robot movement in real time. In the traditional task-implementation mode for industrial robots, the task planning and implementation are taught manually and hence the task accuracy largely depends on the repeatability of industrial robots. The proposed DPT scheme can realize automatic preplanned task and improve the tracking accuracy with eye-to-hand photogrammetry measurement feedback. Moreover, an adaptive Kalman filter is proposed to obtain smooth pose estimation and reduce the influence caused by image noise, vibration, and other uncertain disturbances. Due to high repeatability of the photogrammetry sensor, the proposed DPT scheme can achieve a high path tracking accuracy. The developed DPT scheme can be seamlessly integrated with the industrial robot controller and improve the robot's accuracy without retrofitting with high-end encoder. By using C-track 780 from Creaform as the photogrammetry sensor, the experimental tests on Fanuc M20-iA with the developed DPT scheme demonstrate the tracking accuracy is significantly improved (±0.20 mm for position and ±0.10 deg for orientation).

This paper presents an online image-based visual servoing (IBVS) controller for a 6-degrees-of-freedom (DOF) robotic system based on the robust model predictive control (RMPC) method. The controller is designed considering the robotic visual servoing system's input and output constraints, such as robot physical limitations and visibility constraints. The proposed IBVS controller avoids the inverse of the image Jacobian matrix and hence can solve the intractable problems for the classical IBVS controller, such as large displacements between the initial and the desired positions of the camera. To verify the effectiveness of the proposed algorithm, real-time experimental results on a 6-DOF robot manipulator with eye-in-hand configuration are presented and discussed.

Trajectory planning is a useful technique in robotics for guiding the robot through complicated tasks. In this paper, a new semi-offline trajectory planning method is developed to perform image-based visual servoing (IBVS) tasks for a 6 DOFs robotic manipulator system. This method extends the operation range of the system compared with the traditional IBVS controllers. In this method, the camera's velocity screw is parametrized using time-based profiles. The parameters of the velocity profile are then determined by minimizing the cost function consisting of the error between the initial and desired features while respecting the system constraints. A depth-estimation algorithm is proposed to provide the trajectory planning algorithm with a good estimation of the initial depth. The algorithm for planning the orientation of the robot is decoupled from the position planning of the robot. This method eliminates the limitation caused by camera's field of view. The algorithm is validated via the experiment on a 6 DOFs Denso robot in an eye-in-hand configuration. The experimental results demonstrate that the proposed method can overcome some major IBVS drawbacks such as surpassing the system limits and causing instability of the system in fulfilling the tasks which require a 180° rotation of the camera about its center.

In this article, a novel manipulability-based optimal rapidly exploring random tree (RRT*) path planning strategy is proposed for industrial robot manipulators. When sampling in the search space, two constraints, namely, path length and manipulability measure, are imposed to find a minimal-cost path connecting the start and goal points. By tracking the generated path, a robot manipulator's end-effector can traverse the workspace with a shorter length and, meanwhile, avoid configuration singularities. A constrained closed-loop inverse kinematics technique is utilized to exploit the kinematic redundancy to assign a higher manipulability to an end-effector position. Additionally, the metrics of path length and manipulability measure are used to determine the adaptive step size for the RRT* planner. This helps the space-filling tree to grow efficiently toward unsearched areas and find an optimal path. Simulation analysis and experimental results of a six-degree-of-freedom FANUC-M-20iA industrial robot illustrate the efficiency of the proposed path planning methods.

The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and coupling dynamics. The parameters of the LQR are tuned using Genetic Algorithm (GA). Jacobian linearization method is used to linearize the system around operating-point. Due to the noise of the sensor in the experimental setup, a state observer is designed to observe the velocity of the ball. In order to compare the performance of the LQR and study the effect of simplifying assumptions, two control strategies are designed and implemented: Proportional Derivative Integral (PID) as non-model based control strategy, hybrid PID and Linear Quadratic Regulator (LQR) as combination of model based and non-model based control strategies. The experimental results of this research prove the model based control strategies outperforms the non-model based or hybrid controllers in a nonlinear and noisy ball and beam system. In addition, it is shown that the coupling dynamics cannot be eliminated as a simplifying assumption in designing the controller.