Efficiently coordinating multiple robots to collaboratively complete a set of tasks requires solving two tightly coupled problems: task assignment and collision-free path planning. A widely used formalism for this setting is the Multi-Agent Path Finding (MAPF) framework, which computes conflict-free paths for agents navigating from start to goal locations on a discrete graph. Variants such as Task Assignment and Path Finding (TAPF) extend this framework by jointly determining both agent–task pairings and collision-free paths. This dissertation advances the state of the art in MAPF and its extensions by proposing new algorithms that improve computational efficiency, scalability, and real-world applicability in dynamic, physically constrained environments.

The first contribution focuses on improving the computational efficiency of MAPF. We develop an accelerated Conflict-Based Search (CBS) framework operating over structural–semantic topometric maps, where abstracted regions such as intersections and corridors replace dense grid cells. By reasoning about conflicts at the level of region occupancy in continuous time, rather than at individual grid cells, this representation drastically reduces the number of potential conflicts and search nodes while still supporting fine-grained temporal resolution. Additionally, we propose a Hybrid Priority-Based Search (HPBS) algorithm that integrates Priority-Based Search (PBS) and local CBS within a single search framework, combining the efficiency of PBS with the completeness guarantees of CBS. Together, these methods achieve significant speedups while preserving optimal or bounded-suboptimal solution quality.

The second contribution addresses the Task Assignment and Path Finding (TAPF) problem, which extends MAPF by jointly deciding how tasks are distributed among agents and how agents move to execute them. We first propose two decoupled approaches, in which task allocation and path planning are solved sequentially. The Simulated Annealing with recurrent CBS (SA-reCBS) framework combines a metaheuristic assignment process with a recurrent CBS planner to efficiently handle large-scale TAPF instances. The second decoupled method, a cluster-based task assignment and control framework, employs hierarchical clustering, the Hungarian algorithm, and a traveling salesman formulation to compute balanced task allocations and near-optimal routes. In contrast, the Conflict-Based Search with Task Sequencing (CBS-TS) framework represents a coupled formulation that integrates task assignment and path planning within a single optimization loop. By combining Mixed-Integer Linear Programming (MILP) for task sequencing with CBS for conflict resolution, CBS-TS achieves globally optimal flowtime across all agents and tasks.

The final contribution bridges the gap between discrete MAPF theory and physically realizable robot motion. Three complementary methods are proposed to achieve multi-robot motion planning under dynamic and kinematic constraints. The first, Hybrid Conflict-Based Search (HCBS), extends CBS to continuous space by embedding motion primitives directly into the search, using A* for holonomic robots and Hybrid A* for non-holonomic robots with Ackermann steering, together with geometric body-level conflict detection. The second, a Nonlinear Model Predictive Control (NMPC) framework augmented with a reactive Artificial Potential Field (APF) layer, enables real-time position tracking and adaptive collision avoidance in dynamic environments, providing robustness against disturbances and moving obstacles. The third, a trajectory optimization pipeline, refines discrete paths generated by CBS-TS into smooth, dynamically feasible trajectories within safe corridors, enforcing kinematic constraints, maintaining safety margins, and guaranteeing precise task visitation.

Collectively, these contributions establish a unified progression from discrete, combinatorial coordination to continuous, dynamically feasible multi-robot motion planning. Through extensive simulations and experiments with heterogeneous robotic platforms, the proposed methods demonstrate improved scalability, optimality, and robustness, providing a comprehensive bridge between theoretical MAPF frameworks and practical, deployable multi-robot systems.

With the global increase of hydrogen applications and industries comes an increase in the demands for the hydrogen infrastructure, where reliability is essential. Compressors are an important part of the hydrogen infrastructure, with rolling element bearings being a key component. However, hydrogen is known to significantly reduce the mechanical properties of rolling element bearings, a phenomenon called hydrogen embrittlement. Studies report that bearings which are subjected to hydrogen can fail up to ten times faster compared to bearings operating in other conditions. This detrimental effect on bearing performance has led to research within the subject of hydrogen embrittlement in bearing steels. While it is well established in research that hydrogen affects bearings negatively, several research challenges remain. This includes both the fundamental level to further understand the interaction of hydrogen with the steel as well on a more applied level when it comes to the quantification of hydrogen damage, as most of the research available in hydrogen embrittlement has a qualitative character. 

In this research, rolling sliding tribotesting using a micropitting rig (MPR) as well as full bearing testing were performed to further understand the interaction between hydrogen and bearing steel. These tribotests were combined with electrochemical hydrogen charging, thermal desorption mass spectrometry (TDMS) as well as extended material analysis to connect the wear to the hydrogen concentration as well as hydrogen trapping state in the bearing steel. The hydrogen trapping state in the material is of importance, as a lower hydrogen trapping energy is often correlated with a higher potential of hydrogen embrittlement. The results have shown that the cyclic straining of bearing steel during tribotesting can lower the hydrogen trapping energy, highlighting the increased embrittling effect of hydrogen in bearing applications. Another finding was a quantitative correlation between diffusible hydrogen concentration of bearing steel and surface-initiated damage. The hydrogen concentration of bearing steel was systematically varied by different hydrogen pre-charging conditions, which was then followed by tribotesting and wear quantification. Results revealed that at a hydrogen concentration between 1.4-2 wppm, the wear quantity doubled, while the wear mechanisms remained the same. A novel in situ hydrogen charging full thrust bearing test rig, the Hydrogen Embrittlement in Rolling Element Bearings (HERo) rig, has been developed. This test rig offers several advantages compared to test setups previously described in the literature. As a result of in situ charging, the diffusible hydrogen concentration during tribotesting is not decreasing as in conventional setups where pre-charging is performed prior to tribotesting. This allows for a more precise measurement of the influence of hydrogen on bearing steel. Furthermore, the charging in the HERo rig is performed via the backside of the bearing washer, meaning that the electrolyte is never in contact with the wear track, preventing corrosion or alteration of the surface roughness. The experiments performed with the HERo test rig show a significant decrease in bearing runtime under the influence of hydrogen by a factor of 3. While tests without hydrogen charging failed due to lubricant degradation and surface-initiated wear, hydrogen charged tests failed due to sub-surface initiated macropitting with the presence of white etching cracks. When varying hydrogen pre-charging and in situ charging procedures, it was found that longer pre-charging led to earlier bearing fatigue, while the damage mechanism stayed identical for the different hydrogen concentrations. This indicates that a critical hydrogen concentration exists not only for surface-initiated wear as mentioned above, but also for the initiation of sub-surface crack networks and macropitting. Lastly, it was found that hydrogen promoted not only the formation of white etching cracks and white etching areas, but also martensitic decay and formation of dark etching areas. 

In summary, results of the project confirm the detrimental effect of hydrogen on bearing steel, and add quantitative data to the state of knowledge, making it possible to industrially use the research data for more reliable bearings in hydrogen environments. 

The increasing interest in liquid metal cooled nuclear reactors provides technical and scientific challenges, such as the understanding, prevention, and prediction of the degradation of materials in liquid lead. This work examines fretting wear, occurring between the spacer wire wrapped around fuel tubes and between the steam generator tubes and their supports.

Studies of fretting in liquid lead often focus on the corrosive effects of lead and employ varying test methods. Comparing results from specially built equipment and widely used lab-scale test rigs is challenging because the rigs operate over different load ranges and in different environmental conditions. Publications on the topic lack detailed descriptions of wear mechanisms and friction data. At the same time, both short- and long-duration experiments under conditions relevant to nuclear reactor operation are necessary. This work aims to develop a simplified test methodology for new candidate materials intended for liquid-metal environments and to perform fretting tests on selected materials to understand the dominant wear mechanisms.

The modified oscillating friction and wear tester and the developed methodology were shown to be suitable for investigating fretting in liquid lead. The waviness of the test specimen surfaces significantly influences friction and wear behavior. Lower surface roughness showed superior fretting performance for self-mated Fe-10Cr-4Al, resulting in reduced local contact pressures that limit plastic deformation and material removal. The findings emphasize the importance of surface topography control in mitigating fretting damage in components.

The tribopair with one surface laser-clad with Fe-10Cr-4Al exhibited the lowest wear rate among all tested Fe-10Cr-4Al tribopairs. This is attributed to the difference in hardness between the specimens, which promotes distributed plastic deformation and the formation of a more uniform debris layer.

Fretting tests of self-mated Fe-10Cr-4Al alloy were performed using the modified friction and wear tester and a specially built equipment. After tests with an increasing number of cycles, similar wear mechanisms and wear depths were observed at both facilities. A reduced lubricating effect from saturated lead and its limited availability at the contact were shown to be the main damage-accelerating factors.

This PhD thesis advances robotic autonomy by developing novel path-planning and collision-avoidance solutions that enable resilient missions in complex, unstructured real-world environments. The primary contribution is D*+, a risk-aware path planner extending the D*-lite framework for ground and aerial robots. D*+ introduces a risk layer around occupied and unknown spaces, ensuring traversable paths with safety margins while operating on imperfect maps from real data. Its dynamic mapping supports adaptive replanning, enabling exploration missions in unknown environments, and is excellent for waypoint navigation. Real-world trials with a UAV and quadrupedal robot confirm its versatility across diverse scenarios.

The second contribution is the Detect Track and Avoid Architecture (DTAA), which tackles dynamic obstacles using YOLO-based detection, Kalman filter state estimation, and a nonlinear model predictive controller (NMPC) for anticipatory avoidance maneuvers. DTAA effectively handles fast-moving objects while following D*+ paths; however, it is limited by a short predictive horizon and susceptible to local minima. To overcome these weaknesses, this thesis introduces A*+T, a distributed, time-dependent multi-agent path planner. Built on an A*framework, A*+T integrates D*+ 's risk layers and DTAA's dynamic obstacle handling, adding a temporal dimension to the planning process, enabling collision checks in time and space. The temporal dimension enables distributed autonomous robots to plan collision-free paths in shared spaces based on other robots' planned paths.

Leveraging shared paths and predicted paths from DTAA, A*+T plans collision-free paths around the dynamic obstacles. Validated through simulations and real-world experiments, A*+T enhances mission readiness for multi-agent scenarios.

Beyond these, the thesis integrates these modules into complete robotic systems, enhancing mission control for large-scale applications. Demonstrations include mining inspections (visual and gas detection) and search-and-rescue missions (locating humans/objects). These original advancements offer robust, practical solutions for robotic navigation, validated through extensive real-world testing, and contribute significantly to autonomous systems in high-stakes environments.

Stormwater runoff is a major vector for the pollution transport. Monitoring its quality is necessary for informing effective management strategies. This thesis focuses on an analytical tool increasingly utilized by both field practitioners and researchers: sensor technology. The workflows surrounding on-site sensor deployment, data handling, and interpretation involve multiple often-overlooked nuances and decisions that are not yet common practice, motivating this systematic effort.

The work began with a critical literature review that situated sensor technology within the context of urban stormwater monitoring. The review showed that sensors are frequently treated as turn-key solutions, revealing a mismatch between their perceived maturity and the actual level of methodological development in the field. It further demonstrated that the limited set of water quality parameters that can be measured directly and continuously in-situ has limited standalone value, and that the primary analytical value of sensor data emerges when it is coupled with modelling approaches, which are commonly used to derive pollutant concentration time series.

Drawing on both the reviewed literature and a multi-year field monitoring campaign incorporating continuous sensor measurements alongside sample-based laboratory analyses, this work systematically investigated the problems and limitations of in-situ water quality sensors. Two principal types of adverse effects were distinguished: loss of data and the introduction of bias and uncertainty. The results show that several of the most frequently encountered problems are amenable to post-validation correction. A comparative evaluation of simple interpolation methods and machine-learning–based reconstruction techniques indicates that interpolation is generally sufficient under moderately dynamic conditions, while machine-learning approaches offer only limited advantages for highly dynamic segments. Comparison of field sensor measurements with laboratory reference analyses revealed parameter-specific responses, with strong agreement observed for electrical conductivity, minor field-induced effects for pH, and substantial, condition-dependent bias for turbidity related to seasonal processes. The literature review indicates that uncertainties associated with analytical context are seldom systematically investigated or quantitatively reported.

Finally, this work quantified how adverse effects propagated into pollutant concentration modelling by analysing the influence of data completeness and field-induced uncertainty. Both conceptual and regression-based models were evaluated, including simple statistical and machine-learning regression models. Model performance was strongly influenced by dataset completeness and diversity, with predictive accuracy deteriorating proportionally to the magnitude of uncertainty in the data. Conceptual buildup-washoff and washoff-only models showed poor performance, whereas higher regression model performance depended primarily on the choice and combination of explanatory variables.

Water-based lubricants (WBLs) are emerging as promising alternatives to conventional oil-based lubricants in electric vehicle (EV) transmission systems, driven by increasing demands for energy efficiency, sustainability, thermal management, and environmental compatibility. OEMs and researchers are striving to minimise frictional, thermal, and power losses in EV gearboxes to maximise driving range and system durability, and WBLs have the potential to meet this demand. Moreover, WBLs offer flexibility in viscosity tuning, higher specific heat capacity, and superior heat transfer capability compared with oil-based lubricants. These characteristics create opportunities to improve cooling performance and support the development of a single e-fluid concept. However, their successful implementation requires a comprehensive understanding of film formation, friction and wear behaviour, system-level efficiency, and material compatibility.

This thesis investigates the feasibility of WBLs for EV transmissions through a series of interconnected studies. It begins with the characterisation of elastohydrodynamic (EHL) film formation and pressure–viscosity relationships, revealing the distinctive film-forming behaviour of WBLs. The effects of water content and evaporation sensitivity on the pressure–viscosity coefficient are examined, and the applicability of classical predictive models, including the Hamrock–Dowson equation, is reassessed. Friction and wear analyses demonstrate that fully formulated WBLs can achieve near-superlubricity with minimal shear heating, facilitated by robust surface–additive interactions under rolling/sliding contact. These laboratory findings are validated through full-scale EV gearbox testing, where WBLs reduce power losses and thermal load, improving overall gearbox efficiency by at least 1.5%. Finally, durability is evaluated via tribocorrosion analysis of bearing steel, highlighting the synergistic interaction between mechanical and chemical wear in aqueous environments.

Overall, this work positions WBLs as viable high-efficiency e-fluids for future sustainable transport, provided that challenges related to water loss and wear are effectively addressed through advanced formulation and system design.

To reduce dependence on fossil resources and move closer to a green transition, industrial value chains must shift from carbon-intensive processes to sustainable biomanufacturing methods. Integrating microbial cell factories into biorefineries will enable the valorisation of renewable and waste-derived resources into a diverse portfolio of bio-based products, supporting a circular bioeconomy framework. In this context, oleaginous microorganisms are attractive platform hosts due to their inherent capacity to accumulate increased levels of high-value microbial oil (single-cell oil, SCO) with applications in the biofuel and nutraceutical industries. 

This thesis investigates how feedstock chemistry, cultivation strategies, and host metabolism affect growth, lipid accumulation, and product composition in different oleaginous platforms, specifically yeasts, microalgae, and thraustochytrids, aiming for a predictable bioprocess design. Leveraging the key phenotypes of each host, three feedstock classes were assessed, namely glucose, volatile fatty acids (VFAs), and hydrophobic substrates. The bioprocess performance of each host was evaluated using both refined and secondary sources, as waste materials often contain compounds that might inhibit cell growth. The waste materials tested include VFAs from the anaerobic digestion of brewer’s spent grain, hydrolysates from lignocellulosic biomass, and waste cooking oil. 

The results demonstrated that strategic tuning of bioprocess conditions can redirect intracellular carbon fluxes, thereby determining the biochemical profile of the biomass. Specifically, biodiesel-grade lipids were obtained from yeast cultivation on VFAs and from heterotrophic microalgal cultivation on VFAs and lignocellulosic hydrolysate in nitrogen-limiting conditions. Odd-chain fatty acids (OCFAs), an emerging class of potential specialty lipids, were induced in yeasts during growth on propionate under nitrogen limitation, and in microalgae on glucose-rich conditions with high nitrogen availability, coinciding with increased synthesis of nutritional microalgal protein. Marine thraustochytrids, as prominent producers of omega-3 fatty acids, successfully assimilated hydrophobic substrates, yet DHA productivity was compromised. Therefore, transcriptomic data were used to investigate the underlying mechanisms regulating carbon partitioning between growth and lipid biosynthesis.

The findings of this thesis define evidence-based bioprocess outcomes that support the integration of microbial cell factories into biorefineries, and provide an industrially relevant foundation for the targeted conversion of heterogeneous waste streams into high-value products.

Autonomous inspection plays a vital role in modern robotics, supporting routine site monitoring to mapping unstructured environments. To achieve this, field robots leverage onboard perception, planning and control systems to interpret their environment and execute complex tasks autonomously and in real time. As robots become increasingly adopted for long-term and large-scale inspection missions, the need for a resourceful agent becomes evident. From the integration of autonomous robots with mining infrastructure to rapid deployment of the systems for enhancing situational awareness to extended interaction between humans and robot on inspection scenes, these applications underscore the importance of a scalable, interpretable and adaptable inspection autonomy. To this end, this thesis investigates the key enablers for autonomous inspection: view planning and scene representation. Specifically, it focuses on: (a) developing a reactive view-planning strategy, (b) constructing interpretable inspection scene representation.

The first contribution introduces the method of reactive view planning for mobile robots. Primarily, the First-Look inspection framework establishes the foundation for visual inspection. It utilizes instantaneous 3D point-cloud data alongside desired photogrammetric constraints to generate an inspection view pose. The planner recursively evaluates this process to provide visual coverage of the inspection target. Building on this, the First-Look planner is extended with an exploration behaviour to form the First-Look Inspect-Explore (FLIE) framework. Through this integration, the FLIE framework addresses visual inspection of distributed and unknown inspection targets in unknown environments. Finally, the thesis investigates a hierarchical inspection framework, combining global view planning over historical maps with adaptive local replanning capability for inspection under environment uncertainty. The second contribution explores the method of constructing an actionable, hierarchical scene graph representation of inspected targets, namely 3D Layered Semantic Graph (3DLSG). Specifically, the inspection scene is composed as an ordered set of nodes and edges, organized into abstract layers and structured in a hierarchical manner. The method encodes sensor, semantic and planning information from the scene as attributes of the nodes and edge set. Through this actionable and interpretable hierarchy, the method addresses semantic-geometric path planning for navigation and informed target-selection during inspection planning.  

Overall, this thesis demonstrates deployment of autonomous robots: Unmanned Aerial Vehicles (UAVs) and Quadrupedal platforms, in subterranean and open-pit mines and outdoor urban environments. In all scenarios, field robots operate autonomously, relying solely on onboard perception, planning and control to complete their inspection tasks.

Acoustic cavitation has been employed in a wide range of applications, from bio-logical processes such as fruit juice pasteurization to chemical processes including thedegradation of contaminants. The phenomenon is based on the formation, growth, andimplosive collapse of bubbles in a liquid under pressure fluctuations generated by alter-nating rarefaction and compression cycles of wave propagation. Upon reaching a criticalsize, these bubbles undergo rapid and often asymmetric collapse, producing localized ex-treme conditions characterized by high temperatures, pressures, and reactive species suchas hydroxyl radicals. These effects provide suitable conditions for process intensificationand have been extensively investigated across various industrial applications.Despite its potential as a green technology, most studies remain at the pilot scale,and the development of efficient industrial-scale reactors continues to present challengesrelated to energy consumption, reactor design complexity, and scalability. Consequently,improving cavitation efficiency while minimizing specific energy input remains a centralobjective in advancing ultrasonic technologies for practical and energy-efficient indus-trial use. Achieving this requires careful consideration of multiple parameters affectingsonication performance, including operating frequency, signal characteristics, impedancematching, and reactor configuration.In addition to chemical degradation, the second application explored in this thesisis fruit juice pasteurization at reduced temperature through acoustic and hydrodynamiccavitation as a hybrid approach to conventional thermal processing. The objective is toenhance microbial inactivation while preserving nutritional and sensory quality. A flowthrough sonicator equipped with eighteen transducers and a venturi orifice consisting offive holes was designed, analyzed through numerical software and evaluated, with partic-ular attention to cavitation intensity, bubble dynamics, and energy distribution withinthe treated medium. Reactor geometry and operational parameters were optimized tomaximize cavitation activity while maintaining energy efficiency. The results demon-strate that controlled acoustic cavitation can significantly improve process effectivenesswhile minimizing thermal damage, contributing to more quality-preserving strategies inindustrial juice pasteurization.

The mitigation of anthropogenic CO2 emissions requires the development of efficient, stable, and economically viable capture technologies beyond conventional amine-based systems. Deep eutectic solvents (DESs) have emerged as promising alternatives due to their tunable properties and high CO2 affinity; however, their practical application is often limited by high viscosity and mass-transfer constraints. This study investigates the design, optimization, and validation of DES-based systems for CO2 capture, with particular emphasis on combining cosolvent addition and immobilization strategies to enhance overall performance.

In the first part, [MEACl][EDA]-based DESs with varying molar ratios were synthesized and evaluated as aqueous CO2 absorbents. An aqueous 40 wt.% [MEACl][EDA] (1:5) system was identified as optimal, exhibiting higher CO2 uptake (22.09 wt.% at 22 ºC and 1 atm), faster absorption kinetics (1.24 mol CO2/(kg sorbent·min) after 2 min at 22 ºC), and comparable viscosity (4.401 mPa·s before and 13.330 mPa·s after CO2 capture) relative to benchmark 30 wt.% aqueous monoethanolamine (MEA) (15.74 wt.% CO2 capture capacity, viscosity of 3.318 mPa·s before and 8.413 mPa·s after CO2 capture), along with good thermal stability and recyclability (~88% regeneration). To further improve performance, a novel hybrid approach was developed by immobilizing 5 wt.% DES within mesoporous silica and dispersing 3 wt.% of the composite in 40 wt.% aqueous DES to form slurries. These hybrid systems demonstrated enhanced CO2 capacity (up to 24.93 wt.% at 22 ºC and 1 atm), improved sorption rates (1.4 mol CO2/(kg sorbent·min) after 2 min at 22 ºC), and acceptable viscosity (7.32 and 21.82 mPa·s before and after CO2 capture) and cyclic stability (~91% recovery).

To study the desorption performance, the strategy was extended to non-aqueous systems by immobilizing 5 wt.% DES within mesoporous silica and dispersing 3 wt.% of the composite in 20 wt.% DES in ethylene glycol as a cosolvent, resulting in slurries with improved desorption kinetics (0.38 mol CO2/(kg sorbent·min) after 2 min at 110 ºC), superior thermal stability, minimal solvent loss, and promising regeneration performance (96.4% recovery). Finally, the practical applicability of the developed systems was validated using real biomass combustion flue gas and synthetic gas mixtures (CO2/N2 and CO2/CH4) over a wide pressure range. The aqueous DES-based systems achieved CO2 removal efficiencies >97%, whereas the non-aqueous systems reached 75-85% at 25 ºC and 1 atm for real flue gas. Pressure-dependent studies using CO2/N2 and CO2/CH4 mixtures showed enhanced CO2 uptake with increasing pressure for all systems, with aqueous slurries consistently outperforming their non-aqueous counterparts, while the latter exhibited additional CH4 uptake due to physical sorption in the ethylene glycol phase.