Evaluating a Data Distribution Service System for Dynamic Manufacturing Environments: A Case Study

AbstractSmall and Medium sized Enterprises (SMEs) in Europe struggle to incorporate industrial robots in their production environments, while large enterprises use these robots for large batch production only. The paradigm shift from mass production to mass personalization decreases batch sizes and changes the approach to implementation of industrial robots in manufacturing environments. It also opens doors for SMEs to further incorporate robots in their production environments. The goal of this research is to evaluate the suitability of a data-centric, distributed, decentralized manufacturing system for cooperation between robots and humans. A case is presented featuring cooperation between robots and humans. A control system is proposed based on distributed intelligence and decentralized control, to handle the rapidly expanding complexity in dynamic manufacturing environments. The communication in such a distributed environment is provided by a Data Distribution Service system; an extendible, flexible approach to communication. Key issues that are encountered in implementing the cooperation into the current industrial environments are identified. The proposed control system is projected on the case and evaluated for application suitability and expected performance

A model for assessment of human assistive robot capability

The purpose of this research is to develop a generalised model for levels of autonomy and sophistication for autonomous systems. It begins with an introduction to the research, its aims and objectives before a detailed review of related literature is presented as it pertains to the subject matter and the methodology used in the research. The research tasks are carried out using appropriate methods including literature reviews, case studies and semi-structured interviews. Through identifying the gaps in the current work on human assistive robots, a generalised model for assessing levels of autonomy and sophistication for human assistive robots (ALFHAR) is created through logical modelling, semi-structured interview methods and case studies. A web-based tool for the ALFHAR model is also created to support the model application. The ALFHAR model evaluates levels of autonomy and sophistication with regard to the decision making, interaction, and mechanical ability aspects of human assistive robots. The verification of the model is achieved by analysing evaluation results from the web-based tool and ALFHAR model. The model is validated using a set of tests with stakeholders participation through the conduction of a case study using the web-based tool. The main finding from this research is that the ALFHAR model can be considered as a model to be used in the evaluation of levels of autonomy and sophistication for human assistive robots. It can also prove helpful as part of through life management support for autonomous systems. The thesis concludes with a critical review of the research and some recommendations for further research

Avoiding space robot collisions utilizing the NASA/GSFC tri-mode skin sensor

Sensor based robot motion planning research has primarily focused on mobile robots. Consider, however, the case of a robot manipulator expected to operate autonomously in a dynamic environment where unexpected collisions can occur with many parts of the robot. Only a sensor based system capable of generating collision free paths would be acceptable in such situations. Recently, work in this area has been reported in which a deterministic solution for 2DOF systems has been generated. The arm was sensitized with 'skin' of infra-red sensors. We have proposed a heuristic (potential field based) methodology for redundant robots with large DOF's. The key concepts are solving the path planning problem by cooperating global and local planning modules, the use of complete information from the sensors and partial (but appropriate) information from a world model, representation of objects with hyper-ellipsoids in the world model, and the use of variational planning. We intend to sensitize the robot arm with a 'skin' of capacitive proximity sensors. These sensors were developed at NASA, and are exceptionally suited for the space application. In the first part of the report, we discuss the development and modeling of the capacitive proximity sensor. In the second part we discuss the motion planning algorithm

Positioning technology for stepwise underground robots

Pipeline robots, borehole robots or exploring robots that work in underground environments can be classified as underground robots. When an underground robot takes a task, tracing and mapping the track of the robot is very important. This project addresses the development of a positioning technique for stepwise underground robots, which have been developed in Durham University. This research is expected to provide a general benefit to stepwise robotic positioning systems rather than a particular robotic or other situation. The initial period of this project was the most difficult. After a few months of literature searching, no suitable positioning technique had been found. Existing techniques are suitable for surface robots, undersea robots or airborne robots but are far away from the application requirements for underground robots. Positioning technology depends on sensor techniques and measurement technologies. The underground environment restricts the use of absolute measurement technologies. Consequently, underground robotic positioning systems heavily rely on relative measurements, which can cause unbounded accumulation of the positioning errors. Moreover, underground environments restrict the use of many high precision sensors because of restricted space and other factors. Hence, the feasibility of developing high, long-term, accuracy underground robotic positioning systems was problematic. Since it was found that there was a lack of research on underground robotic positioning, fundamental investigation became necessary. The fundamentals include the dominant error and the characters of the accumulation of positioning errors. After the investigation of the fundamentals the difficulty and feasibility of developing a high long-term accuracy positioning system was understood more clearly and the key factors to improve the accuracy of a positioning system were known. Based on these, a novel parallel linkage mechanism based approach was proposed. This approach has flexibility in terms of geometrical structure and provides the possibility to improve long-term accuracy of a positioning system. Although parallel linkage mechanisms have drawn a great deal of attention from researchers in passed years, this is the first time a parallel linkage mechanism has been applied to a robotic positioning system. Consequently, new problems were generated by this application of parallel linkage mechanisms. In this project, a Principal Component Analysis (PCA) method is applied to solve the positioning problems and a particular case has been used to show how to solve these problems. Through this case, the advantages of this approach and the feasibility to improve the positioning accuracy is presented. The methodology that can be used to solve the problems for different particular cases can also be used to carry out study for general situations, which have also been illustrated. Many problems still need to be solved. At the end of this thesis, some further problems are discussed. The author of this thesis believes that the proposed approach can be applied to industrial projects in the near future

Qualification of a Collaborative Human-robot Welding Cell

AbstractThis work is focused on evaluating performance of a collaborative robot welding cell developed in our previous research. Such a cell is based on an interactive cooperation between a human supervisor and a welding robot. This approach to organizing a workstation allows to employ robots even in the case of prototypes or small productions. Research on collaborative robots usually focuses on safety issues and on the programming techniques. Present work deals with a complementary problem crucial to industrial applications: the qualification of the welding cell performance in terms of accuracy, repeatability and dependability.In this application, the human worker is responsible for handling of the parts to be assembled and for teaching the robot. The robot is in charge of actual welding. Teaching is executed by demonstration: the teacher shows the welding trajectories with a pointer observed by a motion capture system. The program is generated automatically and executed by the robot. Robots and humans share the same workspace in different times therefore human risk exposure is minimal.Industrial applications of this or similar technology require that the process reliability and capability be assessed. We describe and analyze error accumulation along the entire data flow from the measurement tool, through the reference system transformations, to the actual representation and execution of the robot program

Multi-objective particle swarm optimization for the structural design of concentric tube continuum robots for medical applications

Concentric tube robots belong to the class of continuum robotic systems whose morphology is described by continuous tangent curvature vectors. They are composed of multiple, interacting tubes nested inside one another and are characterized by their inherent flexibility. Concentric tube continuum robots equipped with tools at their distal end have high potential in minimally invasive surgery. Their morphology enables them to reach sites within the body that are inaccessible with commercial tools or that require large incisions. Further, they can be deployed through a tight lumen or follow a nonlinear path. Fundamental research has been the focus during the last years bringing them closer to the operating room. However, there remain challenges that require attention. The structural synthesis of concentric tube continuum robots is one of these challenges, as these types of robots are characterized by their large parameter space. On the one hand, this is advantageous, as they can be deployed in different patients, anatomies, or medical applications. On the other hand, the composition of the tubes and their design is not a straightforward task but one that requires intensive knowledge of anatomy and structural behavior. Prior to the utilization of such robots, the composition of tubes (i.e. the selection of design parameters and application-specific constraints) must be solved to determine a robotic design that is specifically targeted towards an application or patient. Kinematic models that describe the change in morphology and complex motion increase the complexity of this synthesis, as their mathematical description is highly nonlinear. Thus, the state of the art is concerned with the structural design of these types of robots and proposes optimization algorithms to solve for a composition of tubes for a specific patient case or application. However, existing approaches do not consider the overall parameter space, cannot handle the nonlinearity of the model, or multiple objectives that describe most medical applications and tasks. This work aims to solve these fundamental challenges by solving the parameter optimization problem by utilizing a multi-objective optimization algorithm. The main concern of this thesis is the general methodology to solve for patient- and application-specific design of concentric tube continuum robots and presents key parameters, objectives, and constraints. The proposed optimization method is based on evolutionary concepts that can handle multiple objectives, where the set of parameters is represented by a decision vector that can be of variable dimension in multidimensional space. Global optimization algorithms specifically target the constrained search space of concentric tube continuum robots and nonlinear optimization enables to handle the highly nonlinear elasticity modeling. The proposed methodology is then evaluated based on three examples that include cooperative task deployment of two robotic arms, structural stiffness optimization under the consideration of workspace constraints and external forces, and laser-induced thermal therapy in the brain using a concentric tube continuum robot. In summary, the main contributions are 1) the development of an optimization methodology that describes the key parameters, objectives, and constraints of the parameter optimization problem of concentric tube continuum robots, 2) the selection of an appropriate optimization algorithm that can handle the multidimensional search space and diversity of the optimization problem with multiple objectives, and 3) the evaluation of the proposed optimization methodology and structural synthesis based on three real applications

Digital twin based what-if simulation for energy management

The manufacturing sector is one of the largest energy consumers in the industrial world, being the energy consumption by the shop-floor equipment, e.g., robots, machines and AGVs (Autonomous Guided Vehicles), a major issue. The combination of energy-efficient technologies with intelligent and digital technologies can reduce energy consumption. The application of the digital twin concept in the energy efficiency field is a promising research topic, taking advantage of the Industry 4.0 technological developments. This paper presents a digital twin architecture for energy optimisation in manufacturing systems, particularly based on a what-if simulation model. The applicability of the proposed what-if simulation model within the digital twin is presented to promote the efficient energy management of AGVs in a battery pack assembly line case study.info:eu-repo/semantics/publishedVersio