With autonomous vehicles becoming more common and established, there are some problems to overcome before their full potential can be reached. One of these problems is the lack of information about the condition of the road, which traditionally would be acquired from the driver operating the vehicle. Volvo Autonomous Solutions are developing an autonomous hauler, made for operating in off-road workplaces, such as quarries and mines. In these off-road workplaces, road maintenance is limited and often performed only when deemed necessary by a driver. This thesis investigates the issue of detecting irregularities in the road on an autonomous vehicle. To achieve this data from an Inertial Measurement Unit (IMU) and a Global Navigation Satellite System (GNSS) unit mounted on the vehicle is collected, analysed, and classified to find any irregularities in the road. In order to improve confidence in the classification of the irregularities, false positives are reduced by using an occupancy grid solution. The results show that the use of IMU data can be used to detect irregularities and that the use of an occupancy grid increases the confidence of detected irregularities.

A lot of features that need to be scanned underground should not be disturbed, from waterlines to unmarked graves. A non-invasive way of probing underground is Ground-Penetrating Radar (GPR). GPR finds differences in materials with radar waves. However, GPR is human-operated and its position is generally determined with a GPS. In some cases, the presence of a human operator can be dangerous, and in other cases, the GPS is not reliable (i.e. mines, glaciers). Therefore there are situations where an autonomous and non-GPS-reliant solution is preferable. The current state of the autonomous GPR system targeted in this work has a non-intuitive GUI that requires an experienced hand to operate. I present an updated hardware and software platform with an intuitive GUI. This updated autonomous system continuously builds a map of its surroundings with Simultaneous Mapping And Localization (SLAM). SLAM localizes itself within the map through sensor-fused position estimates. After the survey is completed the positions are saved and integrated with radar data to be visualized. Robot Operating System 2 (ROS2) is the software I used that allows communications between hardware components, software systems, and the GUI. The new hardware package uses only one source of power and is built using quick connectors that allow for quick removal from the GPR platform. This system allows for intuitive autonomous survey planning and execution in any field paired with a simple way of visualizing data.

According to the facts presented by The United Nations in their 12th goal - ensure sustainable consumption and production patterns - of the 17 sustainability goals, an estimated third of all food produced each year ends up wasted, in the bins of consumers and retailers or spoiling, due to poor transportation and harvesting practices. Reducing food waste is therefore one of the indispensable tasks to accomplish this goal.   This degree project aims to develop, as proof of concept, an automatic control platform for food waste management and implement it in a form of a prototype. The platform is intended to handle the workflow automatically, taking in food waste, weighing it, getting it photographed for image recognition and transporting it to a specified bin according to the classification of food waste using image recognition. The classification part is, however, not included in this project.    The prototype of the control platform consists of a mechanical construction, a weighing scale, a stepper motor, a servomotor and a Raspberry Pi single-board computer. The mechanical construction was designed using CAD (computer aided design) and made with a 3D printer. On the top of the construction is an open flat platform, for placing food waste, which has two walls on the sides, one opening in the front and a robot arm in the back. The robot arm is moved forward, to offload food waste, and backward by a servomotor. The platform is mounted a base with a stand which supports the mechanical construction. It is rotated using a stepper motor installed in the base and stops at three different postions at 90, 180 and 270 degrees, respectively, which are to represent the three different categories of food waste that is to be classified using image recognition. The Raspberry Pi is the controller that is programmed to coordinate the whole workflow.   The prototype is finished and tested. The test results have shown that it works well as a control platform that can handle the workflow, starting at the position to take in food waste, then weighing it and transporting it to three different specified positions, respectively.

Negotiations of different kinds are used to trade goods and services. Within these, the creation of a signed agreement or contract that is binding for the agreeing parties helps also the gathering of evidence that can be used in case of disputes and for adjudication. Traditionally, contracts are established on paper agreements that are signed by all the involved parties and by a law enforcement entity that ensure its legality in a court of law. These contracts have evolved with the introduction of Information Technology (IT) where the negotiation of goods and services is mainly virtual and/or automatized. The consistency and processing time of the computers allow for negotiations to be more efficient than ever.  Digitalized negotiations allow for auctioning systems providing a mechanism to efficiently match demand and supply in the exchange of goods and services. Such suctioning systems allow multiple users to iteratively or non-iteratively compete against one another to achieve allocative efficiency. Lately, digitalized auctions are implemented using Blockchain systems with the use of Smart Contracts to archieve decentralization. These are implemented as a digital script that may encode any set of rules written as code, with the validity of the code being enforced by the Blockchain's consensus mechanism. These Smart Contracts computations however tend to be expensive when executed and limited by the blocksize.   This thesis studies the creation of digitized negotiation protocols and contract definition following the needs of traditional trading and auctioning systems. We investigate the use of Ricardian Contracts for flexible representation of rights and obligations of entities in the context of circular economy in both single and multi-attribute auctions. We analyze the implication of digitized agreements in the context of data sharing. Furthermore, we analyze how usage control policies can be represented into Ricardian Contracts in the context of intellectual property protection, compliance with regulations, and digital rights management.Finally, we analyze the properties that a system that supports the mentioned models should have and how to implement it in the context of distributed auctioning systems by contrasting available state-of-the-art.  The main contributions of the thesis are: (1) The creation of a multi-attribute auctioning protocol for the circular economy which implements Ricardian Contracts for the representation of rights and obligations. (2) A method to negotiate obligations and access provisions with multi-level Ricardian contracts, and automatically enforce those provisions with access control. (3) A state-of-art analysis on distributed and decentralized auctioning systems where the key properties of auctioning systems are identified and are evaluated against the current implementations.

An autonomous vehicle has to understand the surrounding dynamic environment to make the driving decisions such as lane change by giving way to the dynamic obstacles. Motion planning is a challenging problem for an autonomous vehicle while changing a lane in the complex dynamic environment, as the optimal solution for routing must be computed in real time. This thesis provides a framework for an autonomous vehicle to analyse the lane change possibilities. Information from the digital map and estimation of the surrounding objects evaluate a method to find the trajectory for a lane change. The method involves constrained optimization problem of trajectory planning by incorporating interpolated optimal route as the sampling points. Numerical simulations show the effectiveness of the proposed technique in a scenario emulating a driving context for the autonomous vehicle. The result shows that, in a complex dynamic environment, the innovation of deploying model of the road descriptions, graph search and trajectory planning together makes the proposed method far better than the other methods applied in the researches done before for the lane change.

The following thesis addresses the problem of localizing an outdoor mobile robot and mapping the environment using the state of the art of consumer grade RTK GPS. The thesis investigates limitations and possibilities for sensor fusion to increase reliability and usability. The main subject of research is a robotic lawn mower from Husqvarna, the Automower 430x, connected to existing hardware on the product with an auxiliary real time kinematic global positioning system, the Emlid Reach. The test conducted showed that the auxiliary RTK GPS module is currently unsatisfactory as sole absolute position sensor for the Automower platform, mainly due to inconsistent performance. This thesis is meant as a preliminary study for future use of GNSS sensors for outdoor mobile robots and as a suggestive study of the current performance of the increasingly popular Emlid Reach GPS module.

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This thesis research aims to develop a virtual model utilizing a simulation robot cell comprising one or multiple robots and establishing seamless communication with CMAS (Configurable Multi-Agent System) for control purposes. The successful implementation of this setup yielded significant benefits, particularly in pre-risk assessment for the robot cell in Plug and Produce (P&amp;P) operations. By leveraging the virtual simulation prior to actual deployment, the identification and prevention of collision scenarios within the robot's paths were effectively achieved. The simulation was conducted using ABB Robot Studio software, which was seamlessly connected to CMAS software through the REST API protocol. This integration facilitated efficient data exchange and real-time control, enhancing the overall performance and safety of the robot cell. / <p>21 hp</p>

Industry 4.0 is associated with the technological complexity of designing frameworks for factory automation. In that case, since the robots are used in factory operations, this paper proposes a framework that can be used for (near) real-time applications (RTA) in the robotic system. Real-time application is a time constraint that works within a time frame, making it essential to set up a high-speed system in data computation and processing. Monitoring sensors are exposed to different physical variables, such as noise and vibration temperature, from the system, which leads to inefficient data and delay in the data computations. Adaptive Sampling Algorithm A.S.A. was used to reduce the amount of data to be computed. A self-adaptive software (i.e., Rainbow framework) was used to implement the algorithm—Hardware-in-the-loop (HiL) simulation technique used in the simulation of A.S.A. The hardware used in this scenario is the Speedgoat real-time target machine. The proposed framework was tested in an implementation scenario where the robot system had high latency, above 10ms, and had to be reduced to 5ms and below. The results showed fewer samples were collected from the test signal after implementing the algorithm. Hence reducing high latency and increasing real-time application in robot systems. In summary, the proposed framework could be used to develop real-time applications in robotic systems for Industry 4.0.

To increase production and reduce intermediate storage in the factory a new flexible packaging machine is needed. The main purpose of this project is to design a new flexible and modular packaging machine and to create a new framework to use when designing new machines. Current research shows that no framework or practical guide for designing flexible and modular machines exist. A new framework is developed based on a literature study on flexibility in the industry with past, present, and future concepts. The proposed framework is a step-by-step tree/flowchart where each step has a set of rules/guidelines to follow. Along with the flowchart a help section is written to guide the user through the design steps. Existing industry standards are investigated to find a suitable structure for connecting the modules and the machine in the project. Results show that few standards exist in the connectivity structure for the industry.  To lower maintenance costs and increase operational reliability, predictive maintenance using edge computing is investigated. Using edge computing allows the machine to take advantage of predictive maintenance while being offline which makes it suitable for a moveable machine. Evaluation of predictive maintenance show that it still needs more research, and it is complex to implement, which makes it expensive for non-critical machines.The results of this project are a new proposed framework that was used in the project to design a new packaging machine and a flexible PLC program that is easily adaptable for changing modules. The proposed framework fills a gap in the practical usage of known concepts and was used while designing the new machine to evaluate its usability.