Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are sensors that are widely used for land vehicle navigation. GPS receivers provide position and/or velocity data to any user on the Earth&rsquo / s surface independent of his position. Yet, there are some conditions that the receiver encounters difficulties, such as weather conditions and some blockage problems due to buildings, trees etc. Due to these difficulties, GPS receivers&rsquo / errors increase. On the other hand, IMU works with respect to Newton&rsquo / s laws. Thus, in stark contrast with other navigation sensors (i.e. radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this feature, IMU is used in almost all navigation applications. However, it has some disadvantages such as possible alignment errors, computational errors and instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.). Therefore, a fusion or integration of GPS and IMU provides a more accurate navigation data compared to only GPS or only IMU navigation data. v In this thesis, loosely coupled GPS/IMU integration systems are implemented using feed forward and feedback configurations. The mechanization equations, which convert the IMU navigation data (i.e. acceleration and angular velocity components) with respect to an inertial reference frame to position, velocity and orientation data with respect to any desired frame, are derived for the geographical frame. In other words, the mechanization equations convert the IMU data to the Inertial Navigation System (INS) data. Concerning this conversion, error model of INS is developed using the perturbation of the mechanization equations and adding the IMU&rsquo / s sensor&rsquo / s error model to the perturbed mechanization equation. Based on this error model, a Kalman filter is constructed. Finally, current navigation data is calculated using IMU data with the help of the mechanization equations. GPS receiver supplies external measurement data to Kalman filter. Kalman filter estimates the error of INS using the error mathematical model and current navigation data is updated using Kalman filter error estimates. Within the scope of this study, some real experimental tests are carried out using the software developed as a part of this study. The test results verify that feedback GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In addition, some tests are carried out to observe the results when the GPS receiver&rsquo / s data lost. In these tests also, the feedback GPS/INS integration is observed to have better performance than the feed forward GPS/INS integration.

This thesis presents an investigation of the possibility of using the fixed-point arithmetic in the inertial navigation systems, which use the local level navigation frame mechanization equations. Two square root filtering methods, the Potter's square root Kalman filter and UD factorized Kalman filter, are compared with respect to the conventional Kalman filter and its Joseph's stabilized form. The effect of rounding errors to the Kalman filter optimality and the covariance matrix or its factors conditioning is evaluated for a various lengths of the fractional part of the fixed-point computational word. Main contribution of this research lies in an evaluation of the minimal fixed-point arithmetic word length for the Phi-angle error model with noise statistics which correspond to the tactical grade inertial measurements units.

This thesis explores the integration of IoT protocols to enhance supply chain efficiency and sustainability by developing a flexible automated system. The research covers the integration of a palletization optimizer with a flexible robotic platform, a project conducted in collaboration with OpiFlex and Linköping University. Flexibility and sustainability in production, particularly in the food and beverage industry, are critical yet challenging to achieve. This research addresses these challenges by proposing a system that aligns the output with customer needs by combining these technologies. The research employs a combination of case study and exploratory methodologies. The development approach synthesizes elements from Set-Based Design, Point-Based Design, and Agile development frameworks. The primary research questions focus on identifying the best system architecture for integrating the palletization optimizer with a lower-level automation platform and outlining the steps needed to transform this integration into a commercially viable product. The system includes the optimizer, capable of processing customer orders and configuring products on mixed output pallets, integrated with a flexible robotic system provided by OpiFlex. The work involved evaluating communication protocols, MQTT, OPC UA, and TCP/IP, and designing robust interactions and interfaces between the subsystems. The results demonstrate the system's architecture and interaction protocols.  The thesis concludes with a discussion of the results in comparison to the application scenario and the standards consulted. The conclusion is that the chosen interface practices should remain largely intact but be re-developed using an OPC UA-based architecture. The main reasons for this are its support for both pub/sub and client-server models, increased security, and greater support for enterprise application integration. However, depending on the specific application, the downsides of OPC UA may outweigh its benefits.