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.

No description available.

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.

This project is carried out in the field of motion planning of AGVs for the company AGVE with the aim to automate the obstacle free trajectory generation process. The process of manually generating splines in AutoCAD to achieve obstacle avoidance is replaced by the automatic generation of paths by running a python script. Artificial potential field algorithm is implemented in the python script to achieve obstacle avoidance. Clothoid curve is used to create feasible trajectories for obtaining obstacle free paths. The developed program is tested and proved suitable on three scenarios including a real-life problem encountered by the company. The output obstacle free path can be manipulated using three factors which are alpha, moving the neighbours and manipulating the scaling factor for potential fields in the APF algorithm.

This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

Data-Driven Control (DDC) is an active field of research in modern control theory, providing exciting new methods for both model identification and controller design that leverage data to avoid time-consuming manual methods for identification and controller tuning. This thesis presents a practical application of direct Data-Driven Control where the task was to balance an instrumented bicycle at a constant velocity by controlling the steering angle. Exploring the potential of direct DDC as a stabilisation method for bicycles could improve efficiency and effectiveness in controlling similar unstable and nonlinear systems. This approach could eliminate the need for complex modelling and parameter fine-tuning which are present in alternative methods. The approach involved modifying the hardware and software of an existing instrumented bicycle, collecting data, computing direct DDC feedback gains with different state and exploratory control signal combinations, and evaluating the performance of their associated controllers through practical experiments. The study found that 14 out of 48 controllers managed to balance the instrumented bicycle, with the careful selection of states, exploratory control signals, and sufficient size of the data collection set indicating to be the important factors for successful implementation of direct DDC. The thesis provides valuable insights into the practical implementation of direct DDC on unstable and nonlinear systems and highlights potential areas for future research