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The purpose of this paper is to propose an on-line iterative compensation method combining with a feed-forward compensation method to enhance the assembly accuracy of a metrology-integrated robot system (MIRS).

By the integration of a six degrees of freedom (6DoF) measurement system (T-Mac), the robot’ movement can be tracked with real-time measurement. With the on-line measured data, the proposed iterative compensation for absolute positioning and the feed-forward compensation for relative linear motion are integrated into the assembly process to improve the assembly accuracy.

It is found that the MIRS exhibits good performance in both accuracy and efficiency with the application of the proposed compensation method. With the proposed assembly process, a component can be automatically aligned to the target in seconds, and the assembly error can be decreased to 0.021 mm for position and 0.008° for orientation on average.

This paper presents a 6DoF MIRS for high-precision assembly. Based on the system, a novel on-line compensation method is proposed to enhance the assembly accuracy. In this paper, the assembly accuracy and the corresponding distance parameter are given by a series of experiments as reference for assembly applications.

Nuclear waste tanks need to be cut into pieces before they can be safely disposed of, but the cutting process produces a large amount of aerosols with radiation, which is very harmful to the health of the operator. The purpose of this paper is to establish an intelligent strategy for an integrated robot designed for measurement and cutting, which can accurately identify and cut unknown nuclear waste tanks and realize autonomous precise processing.

A robot system integrating point cloud measurement and plasma cutting is designed in this paper. First, accurate calibration methods for the robot, tool and hand-eye system are established. Second, for eliminating the extremely scattered point cloud caused by metal surface refraction, an omnidirectional octree data structure with 26 vectors is proposed to extract the point cloud model more accurately. Then, a minimum bounding box is calculated for limiting the local area to be cut, the local three-dimensional shape of the nuclear tank is fitted within the bounding box, in which the cutting trajectories and normal vectors are planned accurately.

The cutting precision is verified by changing the tool into a dial indicator in the simulation and the experiment process. The octree data structure with omnidirectional pointing vectors can effectively improve the filtering accuracy of the scattered point cloud. The point cloud filter algorithm combined with the structure calibration methods for the integrated measurement and processing system can ensure the final machining accuracy of the robot.

Aiming at the problems of large measurement noise interference, complex transformations between coordinate systems and difficult accuracy guarantee, this paper proposes structure calibration, point cloud filtering and point cloud-based planning algorithm, which can greatly improve the reliability and accuracy of the system. Simulation and experiment verify the final cutting accuracy of the whole system.

This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy.

A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method.

Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively.

A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

To report on developments in robotic vision by a particular robot manufacturer.

Examines FANUC Robotics' philosophy and history of integrated vision, describes its latest offering, and looks at the specification of the new robot controller.

The new robot controller incorporates image processing hardware and software, including calibration procedures. The intelligent robot responds to changes in its surroundings, eliminating the need for jigs and part‐alignment devices and broadening its capabilities.

Presents the intelligent robot as a practical tool in factory automation.

States that manufacturing lines cannot be used for production during commissioning and that it is therefore always the goal to reduce the necessary efforts to a maximum extent. Starts with a description of the state‐of‐the‐art in this field based on the situation in the automotive industry. Additionally lists RTD fields to overcome these problems. Goes on to present methods and software tools aiming at a tremendous decrease in efforts required today.

The increasing use of robotics within modern factories and workplaces not only sees us becoming more dependent on this technology but it also introduces innovative ways by which humans interact with complex systems. As agent-based systems become more integrated into work environments, the traditional human team becomes more integrated with agent-based automation and, in some cases, autonomous behaviours. This paper discusses these interactions in terms of team composition and how a human-agent collective can share goals via the delegation of authority between human and agent team members.

This paper highlights the increasing integration of robotics in everyday life and examines the nature of how new novel teams may be constructed with the use of intelligent systems and autonomous agents.

Areas of human factors and human-computer interaction are used to discuss the benefits and limitations of human-agent teams.

There is little research in (human–robot) (H–R) teamwork, especially from a human factors perspective.

Advancing the author’s understanding of the H–R team (and associated intelligent agent systems) will assist in the integration of such systems in everyday practices.

H–R teams hold a great deal of social and organisational issues that need further exploring. Only through understanding this context can advanced systems be fully realised.

This paper is multidisciplinary, drawing on areas of psychology, computer science, robotics and human–computer Interaction. Specific attention is given to an emerging field of autonomous software agents that are growing in use. This paper discusses the uniqueness of the human-agent teaming that results when human and agent members share a common goal within a team.

The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole.

Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN.

The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly.

The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology.

This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

Humanoid robot has similar shape and action characteristics as humans, and it can complete some basic tasks instead of humans without changing the human environment, which makes humanoid robot become the best structure and help form for robot to provide services for human beings.

The mobile operation control of humanoid robot is generated by the walking movement of humanoid robot's feet, and the robot's hand and arm complete grasping and other operations together.

On the basis of humanoid robot, the integrated system of software and hardware based on the KM34Z256 humanoid robot is described first, and a series of kinematics discussion on its mobile operation is carried out.

The research based on this project shows that the target recognition and positioning method is not only accurate and of high energy but also can realize the mobile operation of humanoid robot.

Owing to the advance of communications and automation technologies it is now common, that a product is designed in one part of the world, manufactured often in another continent, assembled again somewhere else and sold (with various minor customization changes) everywhere in the world. In other words we need to deal with a variety of combinations of local design and manufacture/ assembly and global design and manufacture/ assembly, on‐demand, truly just‐in‐time. We have to recognize that traditional, mass‐produced, highly‐controlled static and rigid design and assembly methods cannot cope with the increased amount of information, knowledge and inter‐referencing requirements of the customers of this rapidly changing world. The challenge is to assemble products on demand at a high quality and relatively low cost in the forthcoming “Knowledge Age”. This paper propagates the above by shedding some light on the challenges, as well as by offering generic algorithmic solutions to the problem of dynamic scheduling with integrated robot tool management and multimedia support in distributed, lean and flexible assembly and design systems (including flexible assembly systems employing human operators as well as robots) that operate on a globally distributed basis.

Robots have been used on plastic injection moulding machines for many years. Linear robots account for the vast majority of applications but there is a small increase in use of six‐axis articulated arm types for certain applications. This paper discusses the pros and cons for the various types being used and describes the downstream processes that can be incorporated within the automated cell.