The aim of this work is to introduce autonomous robots for collecting waste bins in an officeenvironment. Since the environment will contain humans, therefore, the robot-human interactionis essential so that neither the robot nor humans come in harm’s way. To prevent this, the robotneeds to be able to communicate with the humans, and it has to operate in a safe way in the officeenvironment. To facilitate this there is a need to investigate the state-of-the-art in various fieldssuch as: mapping, path planning and, machine-human interaction. The result is a robot that isable to map an indoor environment and use the map to its advantages for producing a suitable pathfrom any given position to any desired location. While traversing the path, sensor data is gatheredfor obstacle detection and avoidance in order to handle dynamic environments.

This thesis presents a robotic gripper with an intelligent sensor system to grasp objects with an adaptive grasp technique. Two techniques are used, one for small objects and one for large objects. The sensor system is able to detect the object and measure its size to adapt the grasp. Optical motion sensors are used to see when the object is slipping between the fingers which means that more force needs to be applied. This makes it possible to grasp rigid and soft objects without damaging them. The functionality of the gripper was tested on eight objects with various characteristics. The results show that it can adapt the grasp technique and grasping force to the objects’ size and softness. It also shows that adding one more grasp technique made it possible to grasp more objects, compared to a different gripper with a single grasp technique which were used as a foundation for this thesis.

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Testsystem är en stor del i dagen samhälle. Att testa en produkt är viktigt för att se hur länge den håller, extra viktigt att testa produkter är det när produkterna ska användas till välfärdsteknologi oche-hälsa då tekniken ska användas i människors vardag. Phoniro utvecklar produkter och tjänster inom välfärdsteknologi och e-hälsa. Exempel på produkt som tillverkas på Phoniro är digitala lås där testningen främst består av mjukvarutester under förhållanden där en verklig aspekt på användning av produkten är önskad men inte är medräknad. Därför vill Phoniro ha en automatiserad testmaskin som på ett mer korrekt sätt återspeglar verkligheten. Målet med projektet var att testa digitala lås där verklig aspekt på användning av produkten ärmedräknad. Det beslutades att göra en testmaskin som skulle innehålla en räknare för att se hur länge testet har körts och sensorer som ska känna av om något havererar. En testmaskin har tillverkats som kan testa det digitala låset. En räknare visar hur många gånger den har öppnat låset. Testmaskinen stannar om något havererat. Målen men projektet har uppnåtts, erfarenheter projektet har gett är praktiska och teoretiska kunskaper om processen att bygga upp ett testsystem.

Learning techniques are drawing extensive attention in the robotics community. Some reasons behind moving from traditional preprogrammed robots to more advanced human fashioned techniques are to save time and energy, and allow non-technical users to easily work with robots. Learning from Demonstration (LfD) and Imitation Learning (IL) are among the most popular learning techniques to teach robots new skills by observing a human or robot tutor. Flawlessly teaching robots new skills by LfD requires good understanding of all challenges in the field. Studies of imitation learning in humans and animals show that several cognitive abilities are engaged to correctly learn new skills. The most remarkable ones are the ability to direct attention to important aspects of demonstrations, and adapting observed actions to the agents own body. Moreover, a clear understanding of the demonstrator's intentions is essential for correctly and completely replicating the behavior with the same effects on the world. Once learning is accomplished, various stimuli may trigger the cognitive system to execute new skills that have become part of the repertoire. Considering identified main challenges, the current thesis attempts to model imitation learning in robots, mainly focusing on understanding the tutor's intentions and recognizing what elements of the demonstration need the robot's attention. Thereby, an architecture containing required cognitive functions for learning and reproducing high-level aspects of demonstrations is proposed. Several learning methods for directing the robot's attention and identifying relevant information are introduced. The architecture integrates motor actions with concepts, objects and environmental states to ensure correct reproduction of skills. This is further applied in learning object affordances, behavior arbitration and goal emulation. The architecture and learning methods are applied and evaluated in several real world scenarios that require clear understanding of goals and what to look for in the demonstrations. Finally, the developed learning methods are compared, and conditions where each of them has better applicability is specified.

This project describes the process and the result of automating a moment in the production of a wave power generator. The project goal is to improve a stacking tool, as well as simulating this process to be able to use it in the production of stator valleys.   The project started with an investigation and a study of the tool, which has been used in laboratory work at Angstrom Laboratory at Uppsala University. It was necessary to study even the robot, which was used in the automating.   The project started by constructing a prototype based on the design of the old tool. The tool became updated with suction cups and electromagnets. The first task which resulted an advantage of the new prototype was to make the sheets movement more reliable. The disadvantage is that it can only take one sheet at a time which prolongs the stacking time. The second task was to simulate the stackning momentum.   To simulate the entire process of the project, a robot cell model was created in the "ABB-Robotstudio" program. The aim was to develop a specific simulation moment based on a previous research project (Robotized Stacking of the Uppsala University Wave Energy Converter Generator Stator) by Erik Hultman and Marcus Linder at Uppsala University.   The results of the tests showed that the instrument worked as it supposed to do and steering of the stator’s sheets movement was secure.

As personal service robots are expected to gain widespread use in the near future there is a need for these robots to function properly in a large number of different environments. In order to acquire such an understanding this thesis focuses on implementing a depth image based planar segmentation method based on the detection of 3-D edges in video-rate speed on an embedded system. The use of plane segmentation as a mean of understanding an unknown environment was chosen after a thorough literature review that indicated that this was the most promising approach capable of reaching video-rate speeds. The camera used to capture depth images is a Kinect for Xbox One, which makes video-rate speed 30 fps, as it is suitable for use in indoor environments and the embedded system is a Jetson TX1 which is capable of running GPU-accelerated algorithms. The results show that the implemented method is capable of segmenting depth images at video-rate speed at half the original resolution. However, full-scale depth images are only segmented at 10-12 fps depending on the environment which is not a satisfactory result.

In this thesis Stefan investigates how cameras can be used for localization of an agricultural mobile robot. He focuses on relative measurement that can be used to determine where a weeding tool is operating relative a weed detection sensor. It incorporates downward-facing perspective cameras, forward-facing perspective cameras and omnidirectional cameras. Stefan shows how the camera’s ego-motion can be estimated to obtain not only the position in 3D but also the orientation. He also shows how line structures in the field can be used to navigate a robot along the rows.

In this work, I present the performance of a extended target tracking algorithm that utilizes Gaussianprocesses. The extended target tracking algorithm is evaluated on objects corresponding to roadusers, with automotive use in mind. The measurements that denes the object to be tracked arederived from stereo image sensor disparity and is called Stick-pixels or Stixels. The process ofgenerating these measurements are also presented in this work.It consists of two separate methods,one relying on stereo image frames and one purely dened by object characteristics and pose. Theextended target tracking algorithm has been tested on three types of simulated road users, car, cyclistand pedestrian. To evaluate the performance of the target tracking algorithm three measures areused, error in position, orientation discrepancy over time and intersection over union.

In this Master Thesis different approaches to mobile localization within construction environments are investigated. At first an overview of different sensors commonly used within localization is presented together with different map representations and a system consisting of a laser scanner and wheel encoders is chosen. The hardware is prepared for the open source ROS environment and three different algorithms for localization are tested. Two algorithms, Gmapping and HectorSLAM, used for Simultaneous Localization and Mapping, are compared. The best map is then used by a Monte Carlo localization algorithm, AMCL, for autonomous navigation. It is found that HectorSLAM produces the most accurate map, given that the grid refinement level is fine enough for the environment. It is also found that the maximum Kullback Leiber distance, used in AMCL, needs to be calibrated in order to perform a sufficient navigation.