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.

This thesis presents a novel navigation framework established to enable the exploration of planetary subterranean areas with Unmanned Aerial Vehicles (UAVs). The key contributions of this thesis work form a robot-safe rapid navigation framework that utilizes a novel bifurcating frontier-based exploration approach. UAVs (limited to quadrotors in this work) have superior navigation capabilities compared to ground robots in terms of 3D navigation as well as fast and versatile Traversability. Utilizing this advantage, this thesis investigates exploration and path-planning problems and presents novel mission behavior-oriented exploration strategies that are evaluated through either simulation with true physics and atmospheric models of planetary bodies or real-world deployment in subterranean areas.  The work included in this thesis is focused on two main research directions. The first direction establishes a novel coaxial quadrotor design that can operate in the thin atmosphere of Mars and utilize the Mars Coaxial Quadrotor (MCQ) to develop an energy-efficient exploration algorithm that leads to autonomously map Martian underground lava channel through true atmospheric model-based simulations. While the second direction establishes a Rapid Exploration Framework (REF) for the real-world deployment for the exploration of GPS-denied underground environments with UAVs. The contributions in the two directions are merged to develop a field-hardened autonomous exploration pipeline for UAVs that focuses on maintaining the heading vector of the UAV towards the most unknown area ahead of the UAV. While also bifurcating the exploration problem in local and global exploration for rapid navigation towards the unknown areas in the field of view and quickly globally re-positioning to a partially explored area. For navigating to the exploration goal of the UAV, it utilizes an expendable grid-based risk-aware path planning framework (D$^{*}_{+}$) that explicitly models unknown areas as risk and plans paths in safe space and for local obstacle avoidance and control the framework utilizes Artificial Potential Fields (APF) and a nonlinear Model Predictive Control based reference tracking scheme.Based on the learnings from field experiments and limitations of state-of-the-art grid-based planning methods on large-scale maps, the final contribution of the thesis establishes a Grid + Graph oriented Traversability-aware exploration and planning framework. The graph-based exploration method proposed in this thesis utilizes geometric shapes to define local traversable paths for the UAV to navigate to the local exploration goal. While utilizing a traversable graph that incrementally plans paths to the edge vertex of sub-maps in the direction of the global re-position goal. The strategy is evaluated extensively in simulations in subterranean urban, tunnel, and cave environments while it is also tested in real-world deployment at test mines of EPIROC and LKAB in Sweden.

Self-driving cars are steadily becoming a reality by a growing number of driver assistance functions enabled by smart perception sensors. The light detection and ranging (lidar) sensor show great potential for perception tasks due to its precise distance measurements. In order to take advantage of the high precision of a vehicle-mounted lidar, its position relative the vehicle needs to be calibrated. This is known as extrinsic calibration. The aim of this thesis is to investigate how to perform the extrinsic calibration of a static vehicle-mounted lidar in a static environment. In addition, the aim has been to develop a tool for running and customizing calibration simulations. The simulation tool CARLA, with its Python application programming interface (API), was chosen and developed to perform lidar simulations in a created virtual factory environment. The chosen calibration method uses a single vehicle-mounted lidar, targeting three spherical targets, whose known centre points act as reference points for the calibration. From the lidar point cloud a calibration algorithm is applied to find the position of the lidar. The algorithm estimates the centre points of the spherical targets and finds the lidar position by aligning the estimated centre points with the reference centre points. The algorithm includes functions that preprocess, cluster, fit spheres and perform point-to-point iterative closest point (ICP). The calibration method showed promising results in terms of point alignment and lidar position estimations. From 1000 simulations of random lidar positions, the average root mean square error (RMSE) of the point alignment was 0.33 mm with a standard deviation of 0.091 mm. The average absolute error of lidar position estimations was for translation [1.0 mm, 0.40 mm, 2.0 mm] with standard deviation [0.20 mm, 0.29 mm, 0.58 mm], and for rotation [0.11°, 0.11°, 0.10°] with standard deviation [0.098°, 0.098°, 0.094°]. Results also showed that uncertainties in the form of noise and point density have an impact on the accuracy of the calibration method. The developed simulation tool in CARLA can be used by other engineers at Veoneer to run customized calibrations or investigate other autonomous driving applications. Another conclusion is that the calibration method provides fast and accurate computations making it a potential candidate for extrinsic calibration.

This report presents various methods for solving the inverse kinematic problem for a non-conventional robotic manipulator with 6 degrees of freedom and discusses their respective advantages and disadvantages. Numerical methods, such as gradient descent, Gauss-Newton and Levenberg-Marquardt as well as heuristic methods such as Cyclic Coordinate Descent and Forward and Backward Reaching Inverse Kinematics are discussed and presented, while the numerical methods are implemented and tested in simulation. An analytical solution is derived for the Saab Seaeye eM1-7 and implemented and tested in simulation. The numerical methods are concluded to be easy to implement and derive, however, lack computational speed and robustness. At the same time, the analytical solution overcomes the same issues, but will have difficulties in singularities. A simple path planning algorithm is presented which plans around singular intervals, making it viable to use the analytical solution without encountering problems with singularities.

As the power distribution system grows and more sensors are added, more data is created every day. This data can be crucial for finding faults, but there is now so much data that it ends up being unused. This presents a valuable opportunity to gain crucial insights into the continuously expanding and increasingly complex power distribution system.  This thesis aims to utilize this valuable resource by finding a feature extraction method that can find valuable features in real-world data, use these features to cluster the data, separate different faults into different clusters, and develop a method for how these clusters can be classified, making it possible for an expert to classify large amounts of data quickly.  In the end, an autoencoder was used for the feature extraction. The features could be used to cluster both labeled and unlabeled real-world data. The clustering also made it possible to find errors in the labeled data, as the data from one class were clustered into two clusters. A method was developed that allowed the clusters of 32454 unlabeled datapoints to be accurately classified in approximately 30 minutes.  This thesis has successfully developed a method that can be used to get insights from large amounts of data, helping experts within the field of power engineering build the power distribution system of the future.

Accurate and robust perception of surrounding objects of interest, such as onroad obstacles, ground surface, curb and ditch, is an essential capability for path planning and localization in autonomous driving. Stereo cameras are often used for this purpose. Comparably, 3D LiDARs directly provide accurate depth measurements of the environment without the need for association of pixels in image pairs. In this project, disparity is used to bridge the gap between LiDAR and stereo cameras, therefore efficiently extracting the ground surface and obstacles from 3D point cloud in the way of 2D image processing. Given the extracted ground points, three kinds of features are designed to detect road structures with large geometrical variation, such as curbs, ditches and grasses. Based on the feature result, a robust regression method named least trimmed squares is used to fit the final road boundary. The proposed approach is verified with the real dataset from a 64-channel LiDAR mounted on Scania bus Klara, as well as the KITTI road benchmark, both achieving satisfying performances in some particular situations. / Exakt och robust perception av omgivande föremål av intresse, såsom hinder på vägar, markytor, trottoarkanter och diken, är en väsentlig förmåga för vägplanering och lokalisering vid autonom körning. Stereokameror används ofta för detta ändamål. I jämförelse, 3D LiDAR ger exakta djupmätningar direkt av miljön utan att behöva matcha pixlar i bildpar. I detta projekt används skillnaden för att överbrygga klyftan mellan LiDAR och stereokameror, och därmed effektivt hitta markytan och hinder från ett 3D-punktmoln genom 2Dbildbehandling. Givet att markytan har hittats, tre typer av funktioner undersöks för att upptäcka vägkonstruktioner med stor geometrisk variation, som trottoarkanter, dike och gräs. Baserat på funktionsresultatet används en robust regressionsmetod, least trimmed squares, för att passa den slutliga väggränsen. Det föreslagna tillvägagångssättet verifieras med två dataset med data från 64-kanalig LiDAR, en från Scania-bussen Klara och KITTI, och uppnår tillfredsställande prestanda i vissa givna situationer.

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