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The purpose of this paper is the presentation and research of a novel robot vision system, which uses laser dynamic triangulation, to determine three-dimensional (3D) coordinates of an observed object. The previously used physical operation principle of discontinuous scanning method is substituted by continuous method. Thereby applications become possible that were previously limited by this discretization.

The previously used prototype No. 2, which uses stepping motors to realize a discontinuous laser scan, was substituted by the new developed prototype No. 3, which contains servomotors, to achieve a continuous laser scan. The new prototype possesses only half the width and turns out to be significantly smaller and therefore lighter than the old one. Furthermore, no transmissions are used, which reduce the systematic error of laser positioning and increase the system reliability.

By using a continuous laser scan method instead of discontinuous laser scan method, dead zones in the laser scanner field can be eliminated. Thereby, also by changing the physical operation principle, the implementation of applications is allowed, which previously was limited by the fixed step size or by the object distance under observation. By using servomotors instead of stepping motors, also a significant reduced positioning time can be accomplished maintaining the relative positioning error less than 1 per cent.

The originality is based on the substitution of the physical operation principle of discontinuous by continuous laser scan. The previously used stepping motors discretized the laser scanner field and thereby produced dead zones, where 3D coordinates cannot be detected. These stepping motors were substituted by servomotors to revoke these disadvantages and provide a continuous laser scan, where dead zones in the field of view get eliminated and the step response of the laser scanner accelerated.

This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects.

This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions.

Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud.

This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

During the past couple of years advances in technology have dramatically improved servomotor torque performance, and reduced their size and weight to one third compared to previous servomotors. This article describes the background, explores new opportunities in machine design and highlights new applications using advanced motion control to make the most of this technology.

Bar‐tacking is a specialized sewing stitch designed to provide immense tensile strength to the garment which requires a high‐speed precision bar‐tacking sewing machine. This paper aims to present an event‐driven multi‐axis cooperative control method for a bar‐tacking sewing machine.

The control method consists of two parts: the multi‐axis cooperative control and the needle stop positioning control. The challenges include the high speed and the precision. For example, the needle must stop at a set position in milliseconds.

The presented multi‐axis cooperative control can ensure the high speed response and the precision of the cooperative control. The needle stop positioning control is based on a combination of the velocity control and the position control with velocity feed‐forward and limitation.

The bar‐tacking sewing machine requires high‐speed start and stop response and coordination of displacement and velocity only at some given points. Therefore, the conventional multi‐axis cooperative control methods are not suitable. In addition, it requires high‐speed precision control under varying loading conditions.

While there are a number of commercial textile machines available in the market, designing a smart bar‐tacking sewing machine with good speed and precision performance remains a challenge.

The bar‐tacking sewing machine requires highly accurate multi‐axes cooperative control. The presented event‐driven multi‐axis control method is effective. It has not only the required high accuracy but also the fast time response.

The proportional‐integral‐derivative (PID) controller has been a practical application in industry due to its simple architecture, being easily designed and its parameter tuning without complicated computation. However, the traditional PID controller usually needs some manual retuning before being used for practical application in industry. The purpose of this paper is to propose an auto‐tuning PID controller (ATPIDC) which can automatically tune the controller parameters based on the gradient descent method and the Lyapunov stability theorem. Finally, a field‐programmable gate array (FPGA) chip is adopted to implement the proposed ATPIDC scheme for possible low‐cost and high‐performance industrial applications, and it is applied to a DC servomotor to show its effectiveness.

To ensure the stability of the intelligent control system, a compensator usually should be designed. The most frequently used compensator is designed as a sliding‐mode control, which results in substantial chattering in the control effort. To tackle this problem, the proposed ATPIDC system is composed of a PID controller and a fuzzy compensator. The PID controller can automatically tune the gain factors of the controller gains based on the gradient descent method, and the fuzzy compensator is utilized to eliminate approximation error based on the Lyapunov stability theorem. The proposed fuzzy compensator not only can remove the chattering phenomena of conventional sliding‐mode control completely, but also can guarantee the stability of the closed‐loop system.

The proposed ATPIDC system is applied to a DC servomotor on a FPGA chip. The hardware implementation of the ATPIDC scheme is developed in a real‐time mode. Using the FPGA to implement, the ATPIDC system can achieve the characteristics of small size, fast execution speed and less memory. A comparison among the fuzzy sliding‐mode control, adaptive robust PID control and the proposed ATPIDC is made. Experimental results verify a better position tracking response can be achieved by the proposed ATPIDC method after control parameters training.

The proposed ATPIDC approach is interesting for the design of an intelligent control scheme. An on‐line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable tracking performance and no chattering phenomena can be achieved by using the proposed ATPIDC system. Also, the proposed ATPIDC methodology can be easily extended to other motors.

This paper aims to present a hybrid actuator controller to obtain fast and stiff position response without any overshoot by blending input signals of a DC servomotor and a particle brake.

The hybrid actuator controller has a module to estimate instantaneous changes in inertia and a blending algorithm that adjusts input signals to the motor and the brake so that together, as a hybrid actuator, they can achieve a fast, stiff position response without overshoot. The control logic implemented in the controller is derived from the kinematics of the system. For the blending algorithm, two separate cases are explored in which the user has the option to either utilize the full‐braking capacity or specify a safe deceleration limit for the system.

The blending algorithm enables the system to operate nearly twice as fast as the motor‐only case without any overshoot or oscillations. The controller can reject inertial load changes and significant external disturbances.

Such hybrid actuators along with the developed controller can be used in robotics and automation to increase the system accuracy and operational speed resulting in higher production rates. In addition, much stiffer haptic force feedback interfaces for virtual reality applications can be designed with smaller actuators. The blending algorithm provides considerable improvements and uses a physics‐based simple and easy‐to‐implement structure.

Extensive efforts have been conducted on the elimination of position sensors in servomotor control. The purpose of this paper is to aim at estimating the servomotor speed without using position sensors and the knowledge of its parameters by artificial neural networks (ANNs).

A neural speed observer based on the Elman neural network (NN) structure takes only motor voltages and currents as inputs.

After offline NNs training, the observer is incorporated into a DSP-based drive and sensorless control is achieved.

Future work will consider to reduce the computation time for NNs training and to adaptively tune parameters on line.

The experimental results of the proposed method are presented to show the effectiveness.

This paper achieves sensorless servomotor control by ANNs which are seldom studied.

The purpose of this paper is to provide new practical viewpoints and a framework regarding the management of technology transition from old to new technology through an in‐depth case study of Fanuc.

Due to the exploratory nature of this research and our interest in identifying key factors that affect the progress of technological transition, the authors selected the grounded theory style of data interpretation.

This case study identifies three key factors common to Fanuc's two major technological transitions and provides a new framework for the successful transition.

This paper provides new practical viewpoints regarding the management of technology transition from old to new technology.