Högskolan i Skövde

To shift the focus from a reactive development approach to a purely proactive approach is a comprehensive and decisive challenge. Reconfigurable manufacturing principles offer a new perspective that emphasizes adaptability over obsolescence. This paper aims to explore how automotive industries include reconfigurability and circularity in production system design to prolong the production system lifetime. In a multiple case study within automotive industry the long-term view in machining system development has been investigated. The results show that the companies try to leave the linear approach behind to look beyond the specific project boundaries and enable a system to be reused over time. There is an awareness of the importance to adopt long-term approaches to achieve a circular mindset and the study reveals that machining systems are characterized by a higher flexibility and higher degree of standardization to enable turbulence in requirements. Still, there are methods required to consider future needs, strategies, and technologies to enable reconfigurable and circular systems. 

This paper presents 'virtual collaborative robot', a virtual reality platform for designing and evaluating collaboration between operators and industrial robots. Within the platform, human-robot collaboration scenarios can be created and a user can interact with a robot without the safety risks that might arise with physical industrial robots. In an initial evaluation of the platform a scenario was implemented combining speech recognition, haptic control, and augmented reality to assemble a car model. The results from this evaluation indicate that the suggested platform can be used to successfully test new applications with the standard equipment of virtual reality headsets.

Human-robot collaboration (HRC) is the concept of combining a human and a robot into the same production cell and utilize the benefits of both. This concept has existed for more than a decade, but there are still quite few implementations of HRC within the manufacturing industry. One reason for this is the lack of knowledge when it comes to suitable tasks for HRC. Current research studies on the topic are mainly based on theoretical reasoning and/or research experiments, and little is known about what the industry perceive as suitable tasks for HRC. Therefore, this paper aims to investigate this and find out what industrial actors thinks are the most value-adding tasks for a human and a robot to carry out together. An in-depth interview study is undertaken with two companies and shop-floor operators, production engineers and automation engineers are interviewed. The result of the study pinpoints a number of tasks that the companies thinks are beneficial for HRC, which can serve as a guideline for other manufacturing companies considering to implement HRC.

Industry is on the threshold of the fourth industrial revolution where smart factories area necessity to meet customer demands for increasing volumes of individualized products. Within the smart factory, cyber-physical production systems are becoming important to deal with changing production. Human-robot collaboration is an example of a cyber-physical system in which humans and robots share a workspace. By introducing robots and humans into the same working cell, the two can collaborate by allowing the robot to deal with heavy lifting, repetitive, and high accuracy tasks, while the human focuses on tasks that need intelligence, flexibility, and adaptability. There are few such collaborative applications in industry today. In the implementations that actually exist, the robots are mainly working side-by-side with humans rather than truly collaborating. Three main factors that limit the widespread application of human-robot collaboration can be identified: lack of knowledge regarding suitable human-robot collaboration tasks, lack of knowledge regarding efficient communication technologies for enabling interaction between humans and robots when carrying out tasks, and lack of efficient ways to safely analyze and evaluate collaborative tasks.

The overall aim of this thesis is to address these problems and facilitate and improve interaction between humans and robots, with a special focus on assembly manufacturing tasks. To fulfill this aim, an assembly workstation for human-robot collaboration has been developed and implemented both physically and virtually. A virtual reality platform called ViCoR has been developed that can be used to investigate, evaluate, and analyze the interaction between humans and robots and thereby facilitate the implementation of new human-robot collaboration cells. The workstation developed has also been used for data collection and experiments during the thesis work, and used to extract knowledge of how the interaction between human and robot can be improved.

Industrin är på väg in i den fjärde industriella revolutionen, där smarta fabriker är nödvändigt för att möta kundernas krav på ökande volymer av individualiserade produkter. Inom den smarta fabriken blir cyberfysiska produktionssystem viktigt för att hantera den varierande produktionen. Människa-robot samarbete är ett exempel på ett cyberfysiskt produktionssystem där människor och robotar delar arbetsyta. Genom att införa robotar och människor i samma arbetscell kan de samarbeta där roboten kan hanterauppgifter som kräver tunga lyft, repetitiva rörelser och hög precision medan människan kan fokusera på uppgifter som kräver intelligens, flexibilitet och anpassningsförmåga. I dagens industri är sådana samarbetsapplikationer få och I de implementationer som finns så arbetar robotarna mestadels i närheten av en människa istället för att faktiskt samarbeta. Tre huvudfaktorer har identifierats som har begränsat antal tillämpningar av människa-robot samarbete: brist på kunskap om lämpliga människa-robot samarbetsuppgifter, brist på kunskap om kommunikationstekniker som möjliggör interaktion mellan människor och robotar samt brist på effektiva och säkra sätt att analysera och utvärdera samarbetsuppgifter.

Det övergripande syftet med denna avhandling är att adressera dessa problem samt att underlätta och förbättra interaktionen mellan människor och robotar, med ett särskilt fokus på monteringsuppgifter. För att uppfylla detta mål har en arbetsstation för samarbete mellan människa och robot utvecklats och implementerats både fysiskt och virtuellt. En virtuell verklighetsplattform som heter ViCoR har utvecklats som kan användas för att undersöka, utvärdera och analysera interaktionen mellan människor och robotar och därigenom underlätta arbetet att implementera nya samarbetsceller. Den utvecklade arbetsstationen har också använts för datainsamling och experiment under avhandlingen och använts för att utvinna kunskap om hur samverkan mellan människa och robot kan förbättras.

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This paper presents a new, more efficient, algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the paper, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

Industrial robot manufacturers have in recent years developed collaborative robots and these gains more and more interest within the manufacturing industry. Collaborative robots ensure that humans and robots can work together without the robot being dangerous for the human. However, collaborative robots themselves are not enough to achieve collaboration between a human and a robot; collaboration is only possible if a proper communication between the human and the robot can be achieved. The aim of this paper is to identify and categorize technologies that can be used to enable such communication between a human and an industrial robot.

Augmented reality smart glasses (ARSG) are increasingly popular and have been identified as a vital technology supporting shop-floor operators in the smart factories of the future. By improving our knowledge of how to efficiently evaluate and select ARSG for the shop-floor context, this paper aims to facilitate and accelerate the adoption of ARSG by the manufacturing industry. The market for ARSG has exploded in recent years, and the large variety of products to select from makes it not only difficult but also time consuming to identify the best alternative. To address this problem, this paper presents an efficient step-by-step process for evaluating ARSG, including concrete guidelines as to what parameters to consider and their recommended minimum values. Using the suggested evaluation process, manufacturing companies can quickly make optimal decisions about what products to implement on their shop floors. The paper demonstrates the evaluation process in practice, presenting a comprehensive review of currently available products along with a recommended best buy. The paper also identifies and discusses topics meriting research attention to ensure that ARSG are successfully implemented on the industrial shop floor.

In recent years human-robot collaboration has been an important topic in manufacturing industries. By introducing robots into the same working cell as humans, the advantages of both humans and robots can be utilized. A robot can handle heavy lifting, repetitive and high accuracy tasks while a human can handle tasks that require the flexibility of humans. If a worker is to collaborate with a robot it is important to have an intuitive way of communicating with the robot. Currently, the way of interacting with a robot is through a teaching pendant, where the robot is controlled using buttons or a joystick. However, speech and touch are two communication methods natural to humans, where speech recognition and haptic control technologies can be used to interpret these communication methods. These technologies have been heavily researched in several research areas, including human-robot interaction. However, research of combining these two technologies to achieve a more natural communication in industrial human-robot collaboration is limited. A demonstrator has thus been developed which includes both speech recognition and haptic control technologies to control a collaborative robot from Universal Robots. This demonstrator will function as an experimental platform to further research on how the speech recognition and haptic control can be used in human-robot collaboration. The demonstrator has proven that the two technologies can be integrated with a collaborative industrial robot, where the human and the robot collaborate to assemble a simple car model. The demonstrator has been used in public appearances and a pilot study, which have contributed in further improvements of the demonstrator. Further research will focus on making the communication more intuitive for the human and the demonstrator will be used as the platform for continued research.

In recent years robots have become more adaptive and aware of the surroundings which enables them for use in human-robot collaboration. By introducing robots into the same working cell as the human, then the two can collaborate by letting the robot deal with heavy lifting, repetitive and high accuracy tasks while the human focuses on tasks that needs the flexibility of the human. Collaborative robots already exists today in the market but the usage of these robots are mainly to work in close proximity.

Usually a teaching pendant is used to program a robot by moving it using a joystick or buttons. Using this teaching pendant for programming is usually quite slow and requires training which means that few can operate it. However, recent research shows that there exist several application using multi-modal communication systems to improve the programming of a robot. This kind of programming will be necessary to collaborate with a robot in the industry since the human in a collaborative task might have to teach the robot how to execute its task.

This project aims to introduce a programming-by-guidance system into assembly manufacturing where the human can assist the robot by teaching the robot how to execute its task. Three technologies will be combined, speech recognition, haptic control, and augmented reality. The hypothesis is that with these three technologies an effective and intuitive programming-by-guidance system can be used within the assembly manufacturing industry. This project have three main motivators: Allowing workers, with no robot programming expertise, to teach the robot how to execute its task in an assembly manufacturing system; Reducing the development time of the robot by introducing advanced programming-by-guidance technology; Showing that augmented reality can add additional information that is useful when programming the robot.

This paper describes a study on efficient optimisation of real-world product sequencing problems with the aim of finding robust solutions. Robust solutions are insensitive to unforeseen disturbances in a manufacturing process, which is a critical characteristic for a successful realisation of optimisation results in manufacturing. In the paper, the traditional method of achieving robust solutions is extended by using standard deviation as an additional optimisation objective. This transforms the original single-objective optimisation problem into a multi-objective problem. Using standard deviation as an additional objective focuses the optimisation on solutions that have both high performance and a high degree of robustness (that is, a low standard deviation). In order to optimise the two objectives simultaneously, a multi-objective evolutionary algorithm based on the Pareto approach is used. The multi-objective method for increased robustness is evaluated using both a benchmark problem and a real-world test case. The real-world test case is from GKN Aerospace in Sweden which manufactures components for aircraft engines and aero-derivative gas turbines. Results from the evaluation show that the method successfully increases the robustness while maintaining high performance of the optimisation.