The technology base for agile manufacturing

The effective use of information is a critical problem faced by manufacturing organizations that must respond quickly to market changes. As product runs become shorter, rapid and efficient development of product manufacturing facilities becomes crucial to commercial success. Effective information utilization is a key element to successfully meeting these requirements. This paper reviews opportunities for developing technical solutions to information utilization problems within a manufacturing enterprise and outlines a research agenda for solving these problems

A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks

Robotix-Academy Conference for Industrial Robotics (RACIR) 2019

Robotix-Academy Conference for Industrial Robotics (RACIR) is held in University of Liège, Belgium, during June 05, 2019. The topics concerned by RACIR are: robot design, robot kinematics/dynamics/control, system integration, sensor/ actuator networks, distributed and cloud robotics, bio-inspired systems, service robots, robotics in automation, biomedical applications, autonomous vehicles (land, sea and air), robot perception, manipulation with multi-finger hands, micro/nano systems, sensor information, robot vision, multimodal interface and human-robot interaction.

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Learning for a robot:deep reinforcement learning, imitation learning, transfer learning

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Geometrical Error Analysis and Correction in Robotic Grinding

The use of robots in industrial applications has been widespread in the manufacturing tasks such as welding, finishing, polishing and grinding. Most robotic grinding focus on the surface finish rather than accuracy and precision. Therefore, it is important to advance the technology of robotic machining so that more practical and competitive systems can be developed for components that have accuracy and precision requirement. This thesis focuses on improving the level of accuracy in robotic grinding which is a significant challenge in robotic applications because of the kinematic accuracy of the robot movement which is much more complex than normal CNC machine tools. Therefore, aiming to improve the robot accuracy, this work provides a novel method to define the geometrical error by using the cutting tool as a probe whilst using Acoustic Emission monitoring to modify robot commands and to detect surfaces of the workpiece. The work also includes an applicable mathematical model for compensating machining errors in relation to its geometrical position as well as applying an optimum grinding method to motivate the need of eliminating the residual error when performing abrasive grinding using the robot. The work has demonstrated an improved machining precision level from 50µm to 30µm which is controlled by considering the process influential variables, such as depth of cut, wheel speed, feed speed, dressing condition and system time constant. The recorded data and associated error reduction provide a significant evidence to support the viability of implementing a robotic system for various grinding applications, combining more quality and critical surface finishing practices, and an increased focus on the size and form of generated components. This method could provide more flexibility to help designers and manufacturers to control the final accuracy for machining a product using a robot system