Collaborative robots are a new type of robot that unlike industrial robots are to be used in close relation to humans without physical walls. Due to this, high demands are placed on the safety of collaborative robots as people must not be harmed. To verify the safety between robot and human, collision measurements are made. For the past 10 years, the number of published articles dealing with Human RobotCollaboration has gone from almost none to approximately 3000 published articles per year. It can also be seen that research on collisions together with cooperation between robots and humans has increased during the same period but not at all in the same size. The thesis is made together with Scania which is a truck manufacturer in Sweden. Scania has its own requirements for safety around collaborative robots and they require that collision measurements in the form of pressure and force take place during each implementation of a collaborative robot. The purpose is to provide a broader knowledge of collaborative robots and their safety. To provide knowledge of how collision measurements should be made and with which tools. Using a spiral development model, several practical measurements were performed on a collaborative robot. Other methods used were interviews, literature study, calculations and benchmarking. The results show that the values set in the robot do not always correspond to those obtained during practical feeds. It uses the robot to show permissible forces in a collaborative position, but also many impermissible force measurements have taken place at different switches and at different speeds. The distance between the robot's base and the robot’s arm where the collision cuts make a great impact on the measurements and show that it can be a danger. Running a collaborative robot at high speeds without external safety equipment also demonstrates risks. Pressure measurements show a large uncertainty of the result and the result is also above the permissible limit in several cases. The impermissible pressures indicate danger for people to work in close contact with collaborative robots. The high difference in the measurement result is only for pressure measurements and the result at 100measurements over force gave a low variation.

No description available.

No description available.

The aim of this thesis is to advance and increase the quality of the field of maintenance and exploration with the use of air-based robotic platforms. The fields that will be addressed are focusing on: a) Identification of adhesion system of climbing robots, b) the control of adhesion level and motion of climbing robots, c) the path planning based on constraints posed from platforms’ mobility characteristics and sensor requirements and d) The attitude control of a morphing Micro Aerial Vehicle (MAV) during in-flight structural re-configurations.Towards this envisioned aim, this thesis will present the following main theoretical contri-butions: a) The development and control of a Thrust Vectoring Vortex Climbing Robot (TVV-CR) which utilizes an Electric Ducted Fan (EDF) for achieving simultaneous locomotion and adhesion without the need of active motorized wheels for traversing surfaces of different ori-entations, b) the design, development, and control of a revised Vortex Robot (VR) based on differential steering platform and the ability to dynamically control the force needed for a con-tinuous adhesion, c) a general modeling methodology, extendable to different adhesion control technologies, of the minimum exerted force of a Climbing Robot (CR) required to achieve ad-hesion regardless of surface orientation d) a set-membership identification and Explicit Model Predictive Control (EMPC) for a Vortex Actuation System (VAS) e) the experimental evalua-tion of the VR under the control framework of an EMPC for the estimated via Autoregressive-Moving-Average with eXternal input (ARMAX) identification VAS f) the formulation of a path planning algorithm for the VR operation under an area coverage scenario while considering the surface characteristics and set inspection specifications. g) the design of a switching Model Predictive Control (MPC) to support the online structural reformation of a foldable quadrotor.In the first part of this thesis, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions are presented in the annex.

Today’s automation systems are becoming way more complex in their design and system complexity is increasing. To design, develop and make sure automation system can reach the market as rapidly as possible, requires a new work method to match these new demands and requirements. By working with simulation and modeling of industrial automation systems, is it possible to streamline the development process and maintain a high level of quality.

No description available.

This thesis aims to create and design a way for a unmanned underwater vehicle to navigateunderwater without the need for human interaction in order to carry out inspections of underwaterstructures or caving systems in a safe and effective manner. The inspection of underwater structuresor underwater caving systems is a difficult and expensive process. A control system script wascreated in the open-source framework Robotics Operating System, ROS so that the robot hasenough tools to be able to carry out such an inspection. These tools range from being able tofollow given trajectories, locate, create and move towards given waypoints and avoid obstaclesthat obstructs its path. Two different movement controls were created and utilised in differentparts of the inspection missions. The testing of the vehicle show that it can carry out a fullinspection mission while avoiding obstacles. The robot can with an updated sensor suite and morecomplex obstacle avoidance algorithms navigate in underwater environment.

Accurate self-positioning of autonomous mobile platforms is important when performing tasks such as target tracking, reconnaissance and resupply missions. Without access to an existing positioning infrastructure, such as Global Navigation Satellite Systems (GNSS), the platform instead needs to rely on its own sensors to obtain an accurate position estimate. This can be achieved by detecting and tracking landmarks in the environment using techniques such as simultaneous localization and mapping (SLAM). However, landmark-based SLAM approaches do not perform well in areas without landmarks or when the landmarks do not provide enough information about the environment. It is therefore desirable to estimate and minimize the position uncertainty while planning how to perform the task. A complicating factor is that the landmarks used in SLAM are not known at the time of planning. In this thesis, it is shown that by integrating SLAM and path planning, paths can be computed that are favorable, from a localization point of view, during motion execution. In particular, it is investigated how prior knowledge of landmark distributions, or densities, can be used to predict the information gained from a region. This is done without explicit knowledge of landmark positions. This prediction is then integrated into the path-planning problem. The first contribution is the introduction of virtual landmarks which represent the expected information in unexplored regions during planning. Two approaches to construct the virtual landmarks that capture the expected information available, based on the beforehand known landmark density, are given. The first approach can be used with any sensor configuration while the second one uses properties of range-bearing sensors, such as LiDAR sensors, to improve the quality of the approximation. The second contribution is a methodology for generating landmark densities from prior data for a forest scenario. These densities were generated from publicly available aerial data used in the Swedish forest industry. The third contribution is an approach to compute the probability of detecting pole-based landmarks in LiDAR point clouds. The approach uses properties of the sensor, the landmark detector, and the probability of occlusion from other landmarks in order to model the detection probability. The model accuracy has been validated in simulations where a real landmark detector and simulated Li-DAR point clouds have been used in a forest scenario. The final contribution is a position-uncertainty aware path-planning approach. This approach utilizes virtual landmarks, the landmark densities, and the land-mark detection probabilities, to produce paths which are advantageous from a positioning point of view. The approach is shown to reduce the platform position uncertainty in several different simulated scenarios without prior knowledge of explicit landmark positions. The computed position uncertainty is shown to be relatively comparable to the uncertainty obtained when executing the path. Furthermore, the generated paths show characteristics that make sense from an application point of view. / Positionering av autonoma mobila plattformar är viktigt för att utföra uppgifter som utforskning, målföljning och spaning. När satellitnavigeringssystem, t.ex. GPS, inte kan användas behöver plattformen istället förlita sig på sina egna sensorer för att bestämma var den är. En sådan teknik är simultaneous localization and mapping (SLAM). Ett vanligt tillvägagångssätt i SLAM är att använda sig av stillastående objekt som är lätta att känna igen, så kallade landmärken. Genom att återse dessa landmärken går det att beräkna hur mycket plattformen har rört sig. Landmärkesbaserade SLAM-metoder klarar dock inte av att positionera plattformen om den skulle befinna sig i områden utan tillräckligt informativa landmärken. Det är därför önskvärt att planera en rutt, från start till mål, som tar hänsyn till var det finns landmärken så att detta problem kan undvikas. Rutter som är fördelaktiga ur lokaliseringssynpunkt när de utförs kan fås genom att inkludera SLAM i ruttplaneringen. Dock är det ofta inte känt i förväg vilka landmärken som kan användas, vilket försvårar situationen. Denna avhandling syftar därför till att undersöka hur landmärkesfördelningen kan användas för att förutsäga informationen från en region. I detta arbete introduceras virtuella landmärken som ett sätt att representera information om outforskade regioner under planeringen. Dessa virtuella landmärken konstrueras för att fånga den förväntade informationen om plattformens position, baserat på sen tidigare känd landmärkestäthet. Två tillvägagångssätt för att beräkna den förväntade informationen från dessa virtuella landmärken presenteras. Det första tillvägagångssättet är utvecklat för ett allmänt fall medan det andra är ett förfinat tillvägagångssätt som utvecklats speciellt för sensorer som mäter vinkel och avstånd, såsom LiDAR-sensorer. Metoder för att generera landmärkestätheter från tidigare flygdata i ett skogsscenario presenteras också. Vidare presenteras ett tillvägagångssätt för att beskriva sannolikheten för att detektera cylinderbaserade landmärken i LiDAR-punktmoln. Detektionssannolikheten bestäms genom att utnyttja egenskaperna hos sensorn, landmärkesdetektorn och sannolikheten för ocklusion från andra landmärken men kräver ingen exakt information om var landmärkena är. Tillvägagångsättet har utvärderats med en riktig landmärkesdetektor och simulerade LiDAR-punktmoln i ett skogs-scenario. Slutligen presenteras ett tillvägagångssätt för ruttplanering för att minimera positionsosäkerheten. Detta tillvägagångssätt utnyttjar de introducerade virtuella landmärkena, landmärkestätheter och sannolikheterna för landmärkesdetektion. Med hjälp av tillvägagångssättet kan en rutt planeras från startposition till målposition som minskar plattformens positionsosäkerhet, utan att alla landmärkens positioner behöver vara kända.

Proper interaction is a crucial aspect of team collaborations for successfully achieving a common goal. In recent times, more technically advanced robots have been introduced into the industrial environments sharing the same workspace as other robots and humans which causes the need for human-robot interaction (HRI) to be greater than ever before. The purpose of this study is to enable a HRI by teaching a robot to classify different human facial expressions as either positive or negative using a convolutional neural network and respond to each of them with the help of the reinforcement learning algorithm Q-learning.The simulation showed that the robot could accurately classify and react to the facial expressions under the instructions given by the Q-learning algorithm. The simulated results proved to be consistent in every conducted experiment having low variances. These results are promising for future research to allow for the study to be conducted in real-life environments.

The primary aim of the project is to automate the process of identifying erroneous entries in stop data originating from a given production line. Machines or work stations in a production line may be stopped due to various planned (scheduled maintenance, tool change, etc.) or unplanned (break downs, bottlenecks, etc.) reasons. It is essential to keep track of such stops for diagnosing inefficiencies such as reduced throughput and high cycle time variance. With the increased focus on Industry 4.0, many manufacturing companies have started to digitalize their production processes. Among other benefits, this has enabled production data to be captured in real-time and recorded for further analysis. However, such automation comes with its problems. In the case of production stop data, it has been observed that in addition to planned and unplanned stops, the data collection system may sometimes record erroneous or false stops. There are various known reasons for such erroneous stop data. These include not accounting for the lunch break, national holidays, weekends, communication loss with data collection system, etc. Erroneous stops can also occur due to unknown reasons, in which case they can only be identified through a statistical analysis of stop data distributions across various machines and workstations. This project presents an automated methodology that uses a combination of data filtering, aggregation, and clustering for identifying erroneous stop data with known reasons referred to as known faults. Once the clusters of known faults are identified, they are analyzed using association rule mining to reveal machines or workstations that are simultaneously affected. The ultimate goal of automatically identifying erroneous stop data entries is to obtain better empirical distribution for stop data to be used with simulation models. This aspect, along with the identification of unknown faults is open for future work.