This thesis introduces a Human-Machine Interface (HMI) developed to enhance safety and efficiency in Configurable Multiagent Systems (CMAS) operating in Plug-and-Produce robot cells. The HMI addresses challenges related to flexible CMAS configurations, specifically addressing collision detection difficulties. Through runtime Configuration and coding of CMAS, the HMI identifies safer robot paths to prevent collisions during real-world CMAS operations. The experimental phase involves a virtual environment, demonstrating the HMI's effectiveness in collision prevention during CMAS operations. This research represents a notable advancement in collision-free motion planning for flexible CMAS configurations, offering a valuable tool for operators to operate CMAS in dynamic production settings, fostering safer and more efficient robotic automation across industries

This thesis introduces a novel framework for edge robotics, enabling the advancement of edge-connected and controlled robots. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), generate vast amounts of multi-sensor data and rely on complex algorithms. However, their computational requirements often necessitate large onboard computing units, limiting their flight time and payload capacity. This work presents a key contribution towards the development of frameworks that facilitate offloading computational processes from robots to edge computing clusters. Specifically, we focus on offloading computationally intensive Model Predictive Control (MPC) algorithms for UAV trajectory control. To address the time-critical nature of these procedures, we also consider latency and safety measures. By leveraging edge computing, we can achieve the required computational capacity while minimizing communication latency, making it a promising solution for such missions. Furthermore, edge computing enhances the performance and efficiency of MPCs compared to traditional onboard computers. We evaluate this improvement and compare it to conventional approaches. Additionally, we leverage Docker Images and Kubernetes Clusters to take advantage of their features, enabling fast and easy deployment, operability, and migrations of the MPC instances. Kubernetes automates, monitors, and orchestrates the system’s behavior, while the controller applications become highly portable without extensive software dependencies. This thesis focuses on developing real architectures for offloading MPCs either for controlling the trajectory of single robots or multi-agent systems, while utilizing both on-premises small-scale edge computing setups and edge computing providers like the Research Institutes of Sweden (RISE) in Luleå. Extensive simulations and real-life experimental setups support the results and assumptions presented in this work.

Visual inspection in autonomous robotics is a task in which autonomous agents are required to gather visual information of objects of interest, in a manner that ensures safety, efficiency and comprehensive coverage. It is, therefore, crucial for identifying key landmarks, detecting cracks or defects, or reconstructing the observed object for detailed analysis. This thesis delves into the  challenges encountered by autonomous agents in executing such tasks and presents frameworks for scenarios ranging from operations by multiple spacecrafts in close proximity to celestial bodies in Deep Space to terrestrial deployments of Unmanned Aerial Vehicles (UAVs) for inspection of large-scale infrastructures. The research thus pursues two main directions: Firstly, a novel formation control strategy is developed to enable autonomous agents to perform proximity operations safely, efficiently, and accurately in order to map the surface of Small Celestial Bodies (SCBs). This investigation encompasses control and coordination strategies, leveraging a realistic astrodynamic model of the orbital environment to navigate safely around SCBs. Along this direction, the contributions focus on enabling a distributed autonomy framework in the form of a cooperative stereo configuration between two spacecraft, allowing acquisition of 3D topological information of the candidate SCB. The framework employs a Leader-Follower approach, treating the maintenance of the desired stereo-formation as a 6 Degree-of-Freedom (DoF) nonlinear model predictive control (NMPC) problem. The second research direction focuses on addressing the problem of enabling robotic inspection for terrestrial applications. With the growing demand for efficient and reliable inspection techniques to improve in-situ situational awareness, the research concentrates on addressing the problem of obtaining detailed visual scan of available structures without any a priori knowledge of either the environment nor the structures. Thus, the key contributions of the presented work reside in the implementation of a unified autonomy, with the unification drawing it's root from the merging of two distinct research perspectives: Inspection and Exploration planning. The contribution establishes a novel solution by introducing a map-independent approach with a synergistic formulation of a reactive profile-adaptive view-planner coupled with a hierarchical exploration strategy and an environment-invariant scene recognition module. By integrating exploration and inspection methodologies, this research seeks to enhance the capabilities of UAVs in navigating and inspecting unknown structures in unfamiliar environments.  Through theoretical developments, extensive simulations and experimental validations, this thesis contributes to the advancement of the state-of-the-art in visual inspection with autonomous robots. Moreover, the findings extend current capabilities of autonomous agents in the field of space exploration as well as in disaster response and complex infrastructure inspection.

The stability and safety of robotic systems are heavily impacted by delays and parametric uncertainties due to external disturbances, modeling inaccuracies, reaction forces, and variations in dynamics. This work addresses the effects of parametric uncertainties in the application of payload transportation by robotic systems that involve time delays and state constraints. The problem is split into two research questions: control of a quadrotor UAV in the presence of delays and control of robotic systems with state constraints. The first two papers explore the approaches for remotely operated quadrotors in the presence of delays and uncertainties. Specifically, the first paper surveys the existing methods for controlling a payload-carrying UAV and further presents a class of control techniques in theory that focus on time-delayed systems. The second paper proposes an adaptive control solution for the tracking control of a quadrotor UAV to transport various unknown payloads in the presence of unknown time-varying delays. The proposed controller is robust to modeling uncertainties and does not require knowledge of the uncertainties' bounds. The performance of the controller is verified on a MATLAB-SIMULINK simulated environment. The final three papers deal with enforcing state constraints on tracking control to ensure the safety of the robots in the presence of parametric uncertainties. The third paper exploits state constraints in the post-grasping scenario of the space debris disposal application. This work proposes a robust control for a space robot to follow the desired trajectory without any violation to safely grasp, carry, and release unknown payloads in their respective regions. The controller is tested in a MATLAB-SIMULINK environment with the dynamics of a planar space robot. The fourth paper introduces an adaptive control technique without any a priori knowledge of the system dynamics or the bounds of uncertainties to impose state constraints in control. The proposed controller is designed for a generic Euler-Lagrangian system in the presence of parametric uncertainties, where the state-dependent nature of the uncertainties introduces unboundedness in the overall uncertainty. The controller is validated in simulation using a robotic manipulator in a pick-and-place operation. The final paper proposes an adaptive controller for the tracking control of an experimental planar space robot. The proposed controller enforces constraints on the robot's states and their derivatives on the tracking control for transporting different payloads without any knowledge of the dynamics of the robot or the bounds of the uncertainties. The controller is validated on the experimental space robot. The stability of the proposed controllers is studied analytically using the Lyapunov theory. The results are presented with various plots and numerically analyzed on the metrics of root mean squared errors and peak errors.

Lifting weights, moving large, heavy objects, or maintaining same posture for extended periods of time exposes workers, mostly in the industrial sector, to pressure on their lower backs, which can have a significant negative impact and result in a variety of musculoskeletal problems and discomfort. The use of an exoskeleton can help to protect workers against lower back injuries of this kind. Past studies have been conducted to study the impact of the exoskeleton on upper body and legs with different exoskeleton, while this study will be an additional study which covers the impact on the most essential and used part of human body i.e., lower back (Thoracolumbar fascia). Workers working in the industrial sectors face more health issues and disabilities because of working on uneven surfaces, under uncomfortable positions like bending, squatting, twisting, and stretching which might impose adverse impact on lower back resulting in a higher number of sick leave.  If more people are impacted by disorders caused by lower back pain, the lesser would be the healthy workers available for work leading to shortage of competent workers in the industry. Back discomfort can affect a person's capacity to work; in fact, it's one of the most prevalent causes of temporary or permanent exclusion from the labor force when it comes to sick leave. The total estimated societal costs of low back pain in Sweden in 2001 was €1860 million, which included all medical expenditures as well as lost productivity as a result of the ailment. Estimates place the total economic cost of LBP in Sweden at €740 million, or €78 per person, for all episodes that started in 2011. [1].  This research study used a passive exoskeleton, namely “BackX”, developed by SUITX Inc. and its impact was measured on the body’s lower back in deep squatting and virtual chair position. Electromyography (EMG) sensors were deployed onto the participant's body as a measuring gadget. The investigations also examined the body muscle data of the various volunteers as recorded by the EMG sensors embedded in the thoracic-lumber fascia, a muscle in the lower back with and without exoskeleton.  The 3DSSPP model has also been used in this research to study the impact of force vs angle relation. It showed how much force was exerted on the human's lower back when lifting weights without wearing the exoskeleton.  In addition, using the proper data processing techniques, the signals from the acquired data will be filtered and processed. According to this study, it is possible to minimize skeletal muscle (Thoracolumbar fascia) activity by up to 60% by using these exoskeletons, which will improve the working conditions for the workforce by easing physical strain. The findings of this study will help small and medium enterprises (SMEs) spread the word regarding the advantages of exoskeletons, which will help to increase public awareness.

State and specifically location estimation is a core concept in automation and is a well-researched field. One such estimation technique is Moving Horizon Estimation (MHE). In this thesis, the MHE variant single-shooting estimation will estimate the location and velocity of a moving object. The moving object is equipped with an  Inertial Measuring Unit (IMU)  measuring acceleration and angular velocity. This thesis will explore pre-integrating the IMU measurement on the device attached to the moving object and using them in another device running the MHE. The acceleration and angular velocity measurements are measured in the local frame of reference of the moving object, rotating the measurement to a global frame of reference requires a known rotation of the tracked object, finding this rotation is also a task in this thesis. This thesis found the presented theory ill-equipped to estimate the object's state without an angle measurement, this thesis assumed any such measurement is made from a magnetometer but the solution presented is not biased towards any other method of measuring angles. With the addition of an angel measurement, the estimation performs at a decimeter-level precision for location.

To map the subsurface Ground Penetrating Radar (GPR) can be used in a non-invasive way. It is currently done manually by pushing a wheeled device on a handlebar. This thesis suggests an alternative method using an integrated autonomous solution. To ac- complice that: several sensors were fused to give the robot perception of the world, the ability to localize itself within it, and plan a path to reach the goal. Detecting algorithms were implemented and tested to ensure the robot could handle a dynamic and compli- cated world. The results showed that the robot could independently navigate in a grid pattern conducting GPR surveys while avoiding obstacles and finding a safe route. All this will allow for collecting GPR data with precise localization measurements and in paths more detailed than a human operator could. In addition, it enables the operator to be at a safe distance in dangerous environments and to search large areas.

The increased demands for customization in manufacturing industries require new automation methods. This thesis presents the development of such a method for a cutting procedure of customized mesh trays at WIBE Group. The work aimed to automatically generate robot targets from data extracted from Autodesk AutoCAD and demonstrate the concept in ABB RobotStudio. A literature review was conducted to find an appropriate method for data extraction and conversion into robot targets. This resulted in an approach where data for the cut positions was collected by scripts created with AutoCAD’s inbuilt programming language AutoLISP. The data was then exported to a text file that could be read by the robot in the RobotStudio simulation model. By offsetting a pre-set robot target with the imported data, new robot targets for each cut were created. The developed method was tested for different types of mesh trays and cut positions. The conclusion is that the method is fully working and that the concept is user-friendly, but some improvements are needed before the concept is ready to be put into operation. For example, further measures are needed to ensure that the correct data is collected from AutoCAD and that the concept gets easier to troubleshoot. Furthermore, development work is also needed for aspects outside the purpose of this thesis, such as the robot cell layout and design of the robot tool.

As the vehicle's autonomy level increases, new security systems are added to its functionality so accidents can be avoided. Those security systems can only be reliable and work effectively if an accurate estimation of the vehicle's velocity is available.  Given the importance of the estimation of velocity in vehicles, in this thesis, we used the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) to improve the velocity estimation of a heavy-duty dumper vehicle. Those methods were used to fuse the wheels' speed information and the Inertial Measurement Unit (IMU) readings available from the vehicle. A simulation model of the vehicle was created using Simulink which outputted the ground truth velocities that were used as a reference for comparison with the estimators when the vehicle went through different path patterns that included combinations of going straight, steering, and experiencing excessive wheel slip. Moreover, the sensors were simulated in Simulink as well and they provided the data that was used by the MATLAB scripts that coded the EKF and the UKF. The performance of the estimators was compared with the ground truth velocities by calculating the Root Mean Squared Error (RMSE) in each case. The results from the experiments showed that both the EKF and the UKF performed the same for the used simulation model, however, both improved the velocity estimation by decreasing the RMSE values from 0.46 (estimation using only IMU information) and 0.226 (estimation based only on wheels information) to 0.20. This is evidence that the Kalman Filter variations are a good option to test when the task is estimating the velocity of a vehicle.

This thesis presents an overview and realization of several steps towards system integration and the control design for the robot arm LWA4D by Schunk using MATLAB/Simulink blocks through the adopted hardware and software from dSpace company. The dynamical equations for the manipulator using three and seven degrees-of-freedom will be derived and implemented using MATLAB/Simulink blocks. Moreover, the implementation of model-based controllers based on the system dynamics is implemented and tested both in ideal conditions and under the delays related to CAN communication protocol. In addition, in order to visualize the motion of the manipulator under the model-based controllers, SimMechanics is used alongside plots showing the end-effector position and the generated torques. Information regarding the first steps on how the hardware and software should be set up in order to establish communication between the manipulator and the Simulink blocks is provided as well. Finally, the problems that appeared during the implementation steps and the results obtained are discussed in the last section.