The offshore industry is adopting Autonomous Underwater Vehicles (AUVs), to decrease the costs associated with surveying underwater sites. Despite requiring less human supervision, AUVs still depend on costly servicing from a vessel, before and after deployment. Combining an AUV with an Unmanned Surface Vessel (USV), a survey vessel that operates without humans on board, could drastically reduce operational costs. This thesis focuses on the crucial requirement of this combination: the autonomous docking of the AUV to the USV. Current studies focused on the AUV-USV combination lack effective solutions for docking in rough ocean conditions, while this is key for its operational employability. These studies have explored horizontal docking approaches, while an unexplored alternative is a vertical docking approach from below the USV. This approach may experience reduced influence of waves, as the wave disturbance acting on the AUV and the USV are phase synchronized. The objective of this study is to evaluate the viability of vertically docking an AUV to a USV in rough seas, using the Lobster Scout AUV as a case study for an initial performance evaluation. This study first identified and scoped the key elements for this initial performance evaluation, which included the waves, the USV, the Lobster Scout, the Docking Station (DS), and vertical docking Guidance Navigation and Control (GNC). Furthermore, the scope was limited to the critical docking stage, where the vehicles are within meters of each other. The study used simulation as the primary method to investigate the thesis objective and a 2-Dimensional (2D) model was developed to simulate the system dynamics, which was fitted and verified using a variety of methods, including analytical calculations, mesh convergence studies, experiments, and visual analysis. To determine the viability of the vertical docking approach of the USV-AUV combination, a probability distribution-based method was developed. Viability was expressed in terms of the maximum operational sea state and the estimated operational up-time. Since docking success under the presence of waves is described probabilistically, the requirement was set that at least 991 out of a 1000 docks should be successful. Furthermore, Monte Carlo simulations were used to obtain the docking performance over a variety of sea states, navigational settings, and different guidance and control methods. A target state vertical guidance method using way-points was designed with various Proportional-Integral-Derivative (PID) based controllers to guide the Lobster Scout to the DS. It was also determined whether kinematic prediction or polynomial prediction could improve docking performance and the impact of navigation on docking performance was investigated. It was found that the vertical docking approach shows promise for enabling a reliable AUV-USV combination for the Lobster Scout in medium to rough seas, with a significant wave height of 1.65 m and 3.6 m respectively, resulting in an estimated annual operational up-time of 59 % to 94 % in the North Sea. Furthermore, there is potential to achieve even higher sea states with improved GNC methods. Overall, this study suggests that the vertical docking approach can lead to a viable AUV-USV combination with improved operational employability compared to current docking studies using horizontal approaches, although the author is of the opinion that both approaches require further investigation.

Dealing with inherently unmodeled dynamics and large parameter variations or faults, is a challenging task while controlling robot manipulators. Classical control techniques cannot usually provide satisfactory responses, and often external supervision systems have to be designed to handle the faults. Recent research has shown that active inference, a unifying neuroscientific theory of the brain, bares the potential of intrinsically coping with strong uncertainties in the system, mimicking the adaptability capabilities of humans. However, the current state-of-the-art regarding active inference in robotics is very narrow and limited. This thesis presents a novel active inference controller as a general adaptive fault tolerant solution for control of robot manipulators. The goal of this work is threefold. First, we demonstrate the applicability of active inference in robotics, deriving a control scheme which is computationally efficient and with high performance. Second, we verify the claimed adaptability properties of active inference against a model reference adaptive controller, in a simulated on-line pick and place task with a 7 degrees-of-freedom robot arm. Third, we propose a method to exploit the controller's structure to perform fault detection, isolation and recovery, without the use of external supervision systems. This work showed that not only active inference is applicable to robotics, but it also outperforms the model reference adaptive controller, and it allows to efficiently deal with sensory faults. This thesis represents a leap forward with respect to the current state-of-the-art of active inference for robotics, and it lays the foundations for further research in this direction.

Substantial efforts are being made to make robots more reliable and safe to work around humans. Robots often perform flawless demos in a controlled environment under the supervision of an operator but tend to fail in the real world when deployed for a long period of time due to faults and environmental disturbances. A robotic system is composed of different physical and software components whose characteristics are likely to change over time. Assumptions made about the system during the design phase may change over time, especially when a system is deployed for long periods. Such changes that are often ignored, need to be considered. Environments in which a robot operates are dynamic with high uncertainty and unpredictability. In such scenarios, capabilities such as situational awareness and self-adaptation will be useful to create more robust, resilient and reliable solutions. The objective for this thesis work is to develop a framework which will embed capabilities such as situational-awareness, context-awareness and self-adaptation within a robot. This research provides a novel, reusable and generalised localisation framework called Situation-Aware Self-Adaptive (SASA) localisation framework for robotics application. This framework is developed using knowledge representation and reasoning which will provide a robot with the capability of adapting according to the situation. We have demonstrated the applicability of the SASA framework to a mobile robot localisation use case. In this research work, we have demonstrated the performance of the framework during environmental disturbances due to poor illumination and featureless environment and internal fault due to component failure. We have also demonstrated the reusability, changeability and the consistency of SASA framework. This work showed that the situational-awareness and self-adaptation capability enhances the robot’s localisation ability and provides reliable localisation even in the case of environmental uncertainties and internal faults where conventional localisation systems fail. This thesis represents a leap forward in the direction of creating more reliable and resilient solutions for robotic applications and it lays the foundations for further research in this direction.

In an ever-evolving society, the demand for autonomous robots equipped with human-level capabilities is becoming increasingly imperative. Various factors, such as an aging population and a shortage of labor for repetitive and physically demanding tasks, have underscored the need for capable autonomous robots to assist us in our daily activities. However, despite the recent advancements in robotics, the field still faces significant challenges in delivering on its promises of developing general-purpose robots with human-level capabilities for everyday tasks. This thesis aims to develop control algorithms at different levels of abstraction to achieve more robust, adaptive, and reactive robot behavior for long-term tasks in dynamic environments. Since our ultimate goal is to achieve human-level performance, a natural starting point is to investigate theories of human intelligence and how they can be applied to real robots, such as mobile manipulators. In this regard, one prominent theory is Active Inference, a popular and influential concept that can explain a wide range of cognitive functions, from motor control to high-level decision-making. Active Inference was developed based on the free-energy principle providing an explanation for embodied perception-action loops. While the free-energy principle and Active Inference have garnered significant attention among neuroscientists, their application to robotics remains largely unexplored, presenting an exciting avenue for research in this thesis. At the same time, it is also important to recognize that we should not confine ourselves solely to theories of human intelligence and their inherent limitations. Machines and humans are built upon fundamentally different structures, which opens up possibilities for alternative approaches. Consequently, this thesis also investigates the use of Model Predictive Path Integral Control (MPPI), which stems from a different formulation of free-energy that is not bound to biological assumptions. By exploring the application of Active Inference to low-level robot control and task planning, as well as the utilization of MPPI for motion planning, this thesis provides advancements in robot control at different levels of abstraction. More concretely, this thesis contributes to the following four areas: 1) Lowlevel adaptive and fault-tolerant control, 2) Reactive high-level decision making, 3) Contact-rich motion planning, and 4) Reactive task and motion planning (TAMP)… @en

In this work, we address the challenges of employing robots in the Search-and-Rescue (SAR) domain, where they can benefit rescue workers to quickly obtain Situational Awareness (SA). Missions with autonomous mobile robots are heavily dependent on environmental representations. Representations have been steadily increasing in the richness that they can capture, in addition to geometry we can now represent objects and their interrelations. These richer environmental representations, such as the 3D scene graph, have provided an opportunity to integrate representations with prior knowledge to make them more actionable and improve the SA they provide. The use of such representations for planning is limited. In previous work, the main approach was to augment and extend the scene graph to enable traditional symbolic planning. The limitations of these methods are that they are offline, scale poorly, and require full observability, making them unsuitable for the SAR domain. The main contributions of this work are as follows. First, we propose the behavior-oriented situational graph, a data structure that integrates data-driven perception with prior knowledge following a novel situational affordance schema. This schema connects situations with robot behaviors and mission objectives, allowing for autonomous mission planning. Second, we propose an efficient method to obtain task utilities from the proposed behavior-oriented situational graph through planning. Finally, we propose an exploration component to discover and select tasks online in dynamic environments that are potentially partially observable. This work is evaluated in several simulation scenarios, showing improved efficiency in mission completion compared to offline methods for the specific SAR domain. Finally, the methods are implemented and tested in a real-world scenario using a mobile Spot robot, showing its effectiveness in practice.