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The industrial robot has high repeatability but low accuracy. With the industrial robot being widely used in complicated tasks, e.g. arc welding, offline programming and surgery, accuracy of the robot is more and more important. Robot calibration is an efficient way to improve the accuracy. Previous methods such as using coordinate measurement machines, laser trackers or cameras are limited by the cost, complex operation or the resolution. The purpose of this paper is to propose an approach and calibration equipment to address these issues.

The proposed method relies mainly upon a laser pointer attached on the end‐effector and single position‐sensitive devices (PSD) arbitrarily located on the workcell. The automated calibration procedure (about three minutes) involves aiming the laser lines loaded by the robot towards the center of the PSD surface from various robot positions and orientations. The localization is guaranteed by precise PSD feedback servoing control, which means physically the intersections of each pair of laser lines (virtual lines) are on the same point. Based on the untouched single‐point constraint, the robot joint offset calibration is implemented. Using the authors' proposed approach, a portable, low‐cost, battery‐powered, wireless and automated calibration system was implemented. Error analysis was conducted on the system.

The localization error of the developed calibration system is within 2 μm. The errors in joint space are magnified in PSD plane, and consequently the resolution in the joint space is improved. The standard deviation of the identified parameters was small (10‐2), indicating the stability of the calibration method. Both simulation and experimental results verify the feasibility of the proposed method and demonstrate the developed calibration system can identify joint offset with uncalibrated laser tool parameters.

The paper shows how a portable calibration system for joint offset of industrial robots was developed and how the goal of fast, automated, low‐cost, portable, and high precision calibration methods for joint offset was achieved.

The purpose of this paper is to achieve the trajectory tracking measurement of a moving target based on double position sensitive detectors (PSDs).

In this paper, first, a double PSD-based measurement system including hardware system and software system is built up. Then, the working principle is studied to calculate parameters, and calibration experience is conducted. Finally, this double PSD-based measurement system is used to test angular displacement and axial displacement on the tool magazine and automatic tool changer.

In the experiment, the maximum position error of a space point based on double PSD measurement system is 0.8566 mm, and the average error is 0.4716 mm. These results show that the built double PSD-based measurement system of trajectory tracking of a moving target is reasonable.

Combining the characteristics of the PSD and principles of binocular visual measurement, a non-contact three-dimensional measuring system based on double PSDs is developed. The designed double-based measurement system is quite suitable for measurement of a fast-changing illuminant or in the case that the tracking accuracy is not tight.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.

A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.

Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R² is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.

In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.