Industrial electric Remotely Operated Vehicles (eROVs) rely on cascaded PI controllers whose gains must be re-tuned manually whenever payload, manipulator, or thruster configuration changes—a recurring and costly operational bottleneck. Existing auto-tuning approaches for cascaded controllers have been demonstrated only on linear, well-modelled, single-axis systems, leaving the combination of nonlinear multi-DOF dynamics, constrained thruster allocation, direction-dependent asymmetry, and tether-induced disturbances unaddressed.

This thesis presents a data-driven auto-tuning workflow for cascaded PI control on industrial eROVs. The workflow incorporates a standardised evaluation protocol, safety screening mechanisms, a composite cost function capturing multiple closed-loop performance criteria, and a hyperparameter configuration that generalises across axes and platforms without per-axis adjustment. It is validated on two physically distinct platforms differing by a factor of approximately 4.6 in mass: one in high-fidelity simulation (four independent tuning runs) and one on real hardware at an operational test site (two runs). Across all runs, the auto-tuned gains consistently match or outperform the manually tuned baseline on every axis where the evaluation protocol provides informative signals, with substantial cost reductions observed on both platforms. Residual cases where the workflow does not improve upon the baseline are traced to identifiable limitations in the evaluation protocol rather than in the optimiser itself. The auto-tuned gains for several axes have been transferred to the production vehicle, and independent pilot assessments confirm clearly improved closed-loop behaviour.

The workflow is ready for industrial deployment as a drop-in replacement for manual cascaded PI tuning on eROVs. ... Read more

Robots operating in confined or visually degraded environments require sensing modalities that provide reliable close-range feedback. Biologically inspired whisker sensors address this need: passive and lightweight, they encode contact geometry through time-varying signals. Spiking neural networks (SNNs) form a natural processing counterpart — also biologically motivated, they perform sparse, event-driven computation that mirrors neural processing and promises energy-efficient inference on neuromorphic hardware. Despite this complementary fit, training data for neuromorphic tactile sensing remains scarce, and fully spiking architectures have not been evaluated on whisker-based classification, leaving their practical viability an open question.
To address this gap, we collected two tactile datasets using a 10-whisker MEMS-based sensor array swept across three object geometries under varying contact conditions. A compact fully spiking network with 901 parameters achieved 100% test accuracy, outperforming larger spiking architectures; classification was reliable from as few as 20 timesteps of contact, and raw pressure signals alone were sufficient. Generalization experiments showed that sweep speed had minimal effect on performance, while indentation depth and sweep direction introduced larger domain shifts. Compared to a structurally equivalent non-spiking baseline, the spiking model matched classification accuracy with an estimated 9.5% reduction in overall inference energy under analog input and a potential 95% reduction under spike-encoded input.
These results demonstrate the feasibility of a fully bio-inspired pipeline — from biomimetic whisker sensing to spiking neural inference — for tactile object classification. ... Read more

Diffusion Policies enable imitation learning by generating action sequences through iterative denoising. In robotics, policies are commonly trained centrally by pooling demonstrations into a single dataset, but demonstrations are often distributed across robots or sites and cannot be easily shared due to privacy, ownership, bandwidth, or operational constraints. This thesis studies whether diffusion based navigation policies can be trained with federated learning while maintaining closed loop performance under client heterogeneity and limited local data. We introduce FedDiff, a pipeline combining Diffusion Policy training with Federated Averaging (FedAvg), and evaluate it on a controlled 2D navigation benchmark with low dimensional beam observations.We compare three training regimes: (i) individual (no sharing), where each client trains only on its own data; (ii) centralized training on pooled data; and (iii) federated training via FedAvg. Experiments cover three scenario families: near-IID (clients have similar data distributions) with sufficient data, near-IID with scarce data, and non-IID compositional generalization across distinct navigation primitives. Across two sensing configurations (4-beam and 8-beam), we train 36 models and report success rates on held out start--goal configurations and, where applicable, on environments not used during training. Federated training is most beneficial under data scarcity and, in the 8-beam Near-IID setting, can match or slightly exceed centralized training on average, while single client training remains strongest on its own training room in the 4-beam data sufficient case. In the Non-IID setting, federated and centralized models perform strongly on primitive navigation skills but remain limited on composed environments that require combining those primitives. We also discuss training dynamics and practical sensitivities relevant to real world deployment. ... Read more

Explicit trajectory communication can be used to coordinate multiple robots, but communicating at every planning iteration can lead to congestion of the communication network, increase message delays and message loss. At the same time, collision-free trajectory planning is often formulated as a nonconvex optimization problem, which can converge to different locally optimal solutions across consecutive planning iterations.When this happens, a robots planned motion can switch between distinct high-level avoidance behaviors, such as passing an obstacle on the left versus on the right, which can lead to inefficient or unsafe behavior. Topology-based motion planners address this by explicitly computing multiple candidate motion plans that represent these different passing decisions, each associated with a distinct homotopy class. This work asks how (changes in) homotopy-class representations can trigger communication to reduce communication load while maintaining safe and efficient behavior. Building on the topology-driven trajectory optimization(T-MPC) approach of [ 1], we propose T-DMPC, a topology-aware distributed motion planner in which each robot, computes multiple guidance trajectories in distinct homotopy classes and refines them via parallel local trajectory optimization within the corresponding homotopy classes, selects a solution using a consistent decision rule that prioritizes the previously executed homotopy class, and communicates the selected trajectory using an event-triggered policy. Communication is triggered by homotopy changes and complemented by geometric-deviation and time-based triggers to bound trajectory staleness. In addition, the robots communicate a fallback trajectory during planning failures (e.g. infeasibility). We evaluate T-DMPC on antipodal swap maneuvers with 2 and3 robots in simulation and on physical robots, comparing against T-VMPC (no communication, constant-velocity predictions) and T-AMPC (always communicate). The experiments show that, T-DMPC achieves task duration and traveled distance comparable to T-AMPC and T-VMPC, while reducing communication to about 9.8% (2 robots) and 13.2% (3 robots) of planning iterations in simulation, and 17.8% (2 robots) and 13.2% (3 robots) in real-world experiments, with no observed physical collisions. Ablations however show that topology-change alone is insufficient for safety,motivating the combined trigger design. ... Read more

Autonomous mobile robots have become increasingly capable over the past decades, enabling their use in domains such as logistics, agriculture, and healthcare. This thesis contributes to a collaborative project between the Dutch National Police and the Delft University of Technology, focusing on search and rescue operations in the security domain.

In these safety-critical missions, robots are expected to support human first responders by exploring unknown and potentially hazardous environments, including buildings and natural terrains. To reduce the cognitive and operational burden on human operators, robots must navigate cluttered spaces both autonomously and safely. In the context of this thesis, autonomy refers to onboard decision-making and safety to avoiding collisions despite uncertainties like sensor noise or environmental effects.

This thesis explores autonomous and safe navigation using model predictive control (MPC), a planning and control strategy that predicts future behavior based on a system model and optimizes actions accordingly. MPC’s strength lies in its ability to handle constraints such as actuator limits and obstacle avoidance. However, applying MPC in real-world navigation presents several challenges, which this thesis addresses through the following contributions.

Navigating a mobile robot through cluttered environments requires it to follow dynamic trajectories while avoiding obstacles. Although MPC is well-suited for this task, recent methods for collision-free trajectory tracking often rely on complex mathematical formulations that can be difficult to interpret and apply. Chapter 2 aims to bridge this gap by offering a structured, step-by-step guide. It explains how to model the robot’s navigation problem, formulate the corresponding MPC problem, and establish performance and safety guarantees.

The chapter presents three formulations: a nominal MPC approach for ideal conditions, a robust MPC formulation that accounts for bounded disturbances, and a robust output-feedback MPC formulation that additionally handles measurement noise. Each formulation is supported by theoretical insights and practical considerations. While not exhaustive, the guide is intended to support researchers and practitioners in implementing MPC-based navigation under varying levels of uncertainty.

To enable autonomy, the MPC formulations introduced in Chapter 2 must operate in real time. This is challenging because MPC relies on solving an optimization problem at each time step, which can be computationally demanding. Planning long-term trajectories and computing control commands at high frequency on embedded hardware is especially difficult.

Chapter 3 addresses this by introducing a hierarchical MPC (HMPC) framework that separates planning and control into two layers. The planning MPC handles long-term trajectory generation at a lower frequency, while the tracking MPC focuses on short-term execution at a higher frequency. This separation allows the use of complex nonlinear models in both layers without compromising real-time performance. The tracking MPC layer, freed from long-term planning, can focus on precise tracking, improving stability and responsiveness.

The HMPC framework also includes a method for generating consistent collision avoidance constraints. Its effectiveness is demonstrated through simulations and lab experiments, showing safer and approximately four times faster goal-reaching compared to a single-layer MPC approach.

While the HMPC framework improves autonomy, it leverages a nominal MPC formulation that assumes perfect models and accurate state data. In practice, mobile robots operate in the presence of model uncertainties and noisy measurements, which can lead to constraint violations such as collisions.

Chapter 4 extends the HMPC framework to address these issues by incorporating robust output-feedback MPC into the tracking layer. This extension, called robust output-feedback hierarchical MPC (ROHMPC), provides formal safety guarantees even in the presence of disturbances and measurement noise. Synthesizing the ROHMPC scheme requires knowledge of uncertainty bounds, which are typically unknown.

To overcome this, the chapter introduces an efficient and modular pipeline that estimates these bounds from experimental data, performs necessary offline computations, calibrates constraint tightening to reduce conservatism, and implements the complete control scheme. The pipeline is released as an open-source software package to support reproducibility and future research. Using this pipeline, the chapter demonstrates the successful validation of the ROHMPC framework properties on a simulated quadcopter platform in Gazebo, with reproducible results.

The HMPC and ROHMPC frameworks address autonomy and safety respectively, but successful deployment in real-world scenarios also depends on the reliability of the system. A key aspect of reliability is reproducibility: the ability to consistently generate similar results.

Chapter 5 explores this concept in the context of robotics, defining reproducibility and analyzing how it applies to the hardware-software setups used in earlier chapters. The HMPC framework satisfies method reproducibility, meaning its implementation can be consistently reproduced. However, due to asynchronous processes and non-deterministic code components, it does not fully achieve results reproducibility.

In contrast, the ROHMPC framework satisfies both method and results reproducibility, reinforcing the credibility of the framework. By raising awareness of reproducibility challenges and offering practical insights, this chapter aims to support the development of more robust and trustworthy robotic systems.

All results presented in this thesis have been made publicly accessible through submissions to peer-reviewed venues, an open-access preprint server, and the release of open-source software packages. These results highlight the effectiveness of hierarchical MPC in both simulation and laboratory settings, and demonstrate how formal safety guarantees can be achieved under uncertainty.

To support future research, Chapter 6 summarizes the key contributions and outlines several promising directions for further exploration. These include extending the proposed algorithms to 3D environments, integrating onboard sensing for autonomous outdoor navigation, and incorporating data-driven methods to reduce conservatism. ... Read more

Fully autonomous driving has long been promised, yet remains difficult to deploy at scale. Safe and efficient autonomy hinges on motion planning and control under tight constraints, realistic vehicle dynamics, and reliable cooperation between vehicles, often in the presence of delays, uncertainty, and adversarial communication. Progress in these areas depends on fast design–test cycles, but current validation workflows are polarized, high-fidelity simulation enables rapid iteration, yet struggles to reproduce road–tire interaction, actuator and sensor dynamics, and multi-agent network effects. Full-scale experiments capture these effects, but are costly, slow, and constrained by safety and regulatory requirements. This thesis addresses this gap by enabling physically grounded experimentation by designing a low cost and reproducible robotic platform, and by developing control and coordination methods for cooperative driving applications for more efficient traffic flow, as well as model based control strategies to handle safety critical maneuvers at the limits of handling.

Three limitations in the state of the art motivate the research objectives of this thesis. First, existing small-scale car-like platforms either remain relatively expensive for multi-agent experiments, depend on custom components that hinder reproducibility, or lack rigorous system-identification workflows needed for model-based control and sim-to-hardware transfer. Second, cooperative driving methods, for example platooning, often rely on timely attack detection or hard switching to degraded modes, leaving vulnerability windows and risking undesirable transients, moreover, adversarial robustness at the coordination and topology-management level, such as merge, split, rearrange, is less developed than low-level spacing control. Third, widely used MPCC formulations can become unreliable on highly curved paths because internal progress approximations drift under large deviations, which can degrade constraint handling, racing-oriented MPC variants frequently depend on offline priors, for example precomputed racelines, limiting transfer to emergency-like maneuvers where only local geometry is available. Accordingly, this thesis aims to develop: (I) a reproducible, low-cost experimental platform with a complete identification pipeline, (II) cooperative driving algorithms that preserve safety even under persistent or undetected communication attacks and remain feasible under actuator saturation, and (III) curvature-aware MPCC formulations that remain reliable on tight curvature and avoid dependence on offline priors in high-performance settings.

The thesis begins with the design and characterization of the Delft Autonomous-driving Robotic Testbed (DART). DART is a low-cost, reproducible, small-scale vehicle built on a commercial RC chassis and augmented with additional sensing and computational capabilities, including Lidar, an IMU, custom wheel encoders, and onboard computing. The platform preserves the essential features of full-scale vehicle dynamics, Ackermann steering, suspensions, electric motor, while remaining small enough to work with in laboratory settings. A central component is a comprehensive system identification procedure that yields reliable kinematic and vehicle dynamics models tailored to small-scale vehicles, along with sub-models for motor force, friction, steering actuation, and tyre lateral forces. This modelling pipeline enables realistic testing and accurate model-based control on hardware.

The thesis then shifts focus to cooperative multi-vehicle systems by introducing a distributed, attack-resilient platooning framework. This work addresses two intertwined challenges: maintaining safety and formation integrity under malicious interference on the communication channels, and enabling coordinated platoon-level decisions such as merging, splitting, and rearranging. At the control level, the method combines sensor-based Adaptive Cruise Control with communication-based Cooperative Adaptive Cruise Control, while a safety filter ensures collision avoidance even when communicated acceleration data is corrupted. At the coordination level, a distributed topology-management strategy detects inconsistent information and isolates compromised vehicles by reorganizing the platoon. The approach is validated in simulation and experimentally on multiple DART vehicles, showing that safety and string stability are preserved despite communication attacks.

With this platform at hand, we then focus on designing a motion-planning strategy for urban driving. We investigate model predictive control and develop a Curvature-Aware Model Predictive Contouring Control (CA-MPCC) framework. Traditional MPCC formulations assume low curvature and rely on a lag-error term that couples progression along the reference path with lateral tracking. This complicates tuning and reduces reliability in tight curves. The CA-MPCC formulation resolves these limitations by explicitly accounting for curvature in the path geometry, removing the lag-error term entirely and simplifying both the cost structure and the tuning process. The method is validated in simulation and on the DART platform, demonstrating improved robustness, reduced parameter sensitivity, and reliable real-time performance even on highly curved trajectories.

Next, the thesis extends the curvature-aware MPCC methodology to high-performance domains with the rCA-MPCC framework for autonomous racing. Racing introduces additional challenges, such as operating at the limits of handling, rapid curvature changes, and strong coupling between dynamical states. The rCA-MPCC formulation augments CA-MPCC with a curvature-informed terminal cost and a compact reference-path representation that avoids dependence on precomputed race line information. This chapter also expands the physical vehicle model through actuator-dynamics identification and residual dynamic modeling using Gaussian Processes. Together, these advances improve prediction accuracy and enable robust high-speed control. We furthermore extend the car-racing formulation to aerial drone racing, bridging the gap between the two communities. Experiments on small-scale cars and simulations on quadrotor drones demonstrate faster, more consistent lap times and robustness across different dynamic model choices.

Finally, the thesis demonstrates how the developed platform and control tools can be applied to cooperative multi-robot tasks such as persistent monitoring and target detection. The chapter combines DART with the CA-MPCC controller to implement Lissajous-curve-based coordinated trajectories generated by time-inverted Kuramoto dynamics. The result is a distributed coordination strategy that guarantees collision avoidance and complete area coverage. Experimental validation confirms that the high-level coordination method and low-level CA-MPCC controller integrate smoothly on real hardware, even under disturbances and temporary agent failures.

Overall, this thesis contributes a unified framework for physically grounded experimentation, accurate dynamics modeling, and robust control and coordination in autonomous driving. By developing a reproducible platform, constructing reliable models, and demonstrating advanced control strategies, from emergency maneuvers to cooperative platooning and multi-agent monitoring, it lowers the barrier to real-world validation and accelerates iteration cycles for autonomous driving research. The insights presented here support future work toward safer, more robust, and more efficient autonomous transportation systems. ... Read more

As robots leave factory floors and deploy into unstructured human environments, they must navigate safely and efficiently alongside people and other autonomous systems. In these dynamic settings, decisions are inherently interdependent: a robot's optimal action depends heavily on how others will respond, and vice versa. Anticipating and actively shaping these responses is central to competent multi-agent behavior. However, this interaction unfolds under significant uncertainty, with limited prior knowledge of other agents' objectives, constrained sensing capabilities, and tight computational limits.

This dissertation addresses these fundamental challenges by framing robot motion planning for interactive scenarios through the lens of non-cooperative game theory. This perspective provides a principled mathematical framework for modeling multiple self-interested decision-makers who act simultaneously with partially aligned objectives. Focusing on environments where a single controlled robot interacts with uncontrolled agents whose intents are not known a priori, this dissertation develops a comprehensive suite of game-theoretic tools. These tools enable robots to infer underlying intents from observations and generate motion plans that capture the complex interdependence of self-interested decision-making.

The core contributions of this dissertation span intent inference, real-time adaptation, uncertainty-aware planning, computational efficiency, and complex non-smooth dynamics.

First, we formalize the problem of learning unknown intents from observed past behavior as an inverse game. By casting this as maximum-likelihood estimation with equilibrium constraints, our transcription jointly estimates game parameters, hidden states, and future decisions, significantly improving inference accuracy. Second, we tightly integrate these inverse games with online planning. We propose a novel solution technique handling inequality constraints with a first-order update rule for amortized inference, yielding a game-theoretic planner that dynamically adapts to evolving intent estimates.

Third, we address situations demanding explicit reasoning over a distribution of possible intents. We introduce contingency games, an uncertainty-aware planning technique that jointly generates multi-hypothesis predictions of others alongside conditional plans for the robot. By explicitly anticipating future information gains, this approach seamlessly bridges the gap between conservatively ignoring uncertainty and assuming it will never resolve. Fourth, to alleviate the substantial computational burden of online game-theoretic planning, we introduce an amortized solver for mixed strategies. An offline model learns to propose dynamically feasible trajectory candidates, while a discrete game solved online rapidly computes competitive mixed Nash equilibria.

Finally, we tackle interaction domains with inherently non-smooth dynamics, such as multi-agent manipulation, where constraints are not continuously differentiable. We propose a data-driven approach leveraging probabilistic inference and generative diffusion models. This blends learning from single-agent demonstrations with reasoning about joint multi-agent costs, discovering collaborative strategies without requiring massive multi-agent datasets.

In summary, this dissertation advances interactive motion planning by equipping robots to accurately infer intents, act safely under uncertainty, and navigate complex interactions. These algorithmic contributions are extensively validated via simulation and ground robots across autonomous driving, mobile navigation, and multi-agent manipulation, accompanied by open-source libraries to accelerate future research. ... Read more

As self-driving vehicles progress toward real-world deployment, efficient and reliable motion planning in dynamic multi-agent environments becomes increasingly essential. This work addresses this challenge by advancing the field of nonlinear distributed model predictive control (NMPC) for autonomous multi-vehicle coordination. The approach focuses on complex, interaction-dense scenarios where space is limited. To model vehicle shapes and collision boundaries efficiently it uses polytopic set representations to approximate vehicle geometries and collision boundaries instead of conventional shapes such as ellipsoids.

We propose a novel distributed NMPC approach for navigation in tight environments. The goal is to enable coordinated motion planning for multiple autonomous vehicles in dense traffic scenarios. This can be easily modelled with centralised formulations, however, considering the scale of the road network they become computationally intractable as the number of agents grows. In distributed approaches instead each vehicle solves its local part of the problem which scales linearly and is therefore better suited for these kind of environments. So building on an existing distributed method we enhance its structure to improve scalability, safety, and performance in cooperative autonomous driving tasks. The research is guided by three objectives: (RO1) to reproduce a known distributed baseline algorithm using the DART (Delft’s Autonomous-driving Robotic Testbed) vehicle model \cite{lyons_dart_2024}, the Model Predictive Contouring Control (MPCC) \cite{lam_model_2010} tracking objective, and the ACADOS solver framework \cite{verschueren_acados_2020}; (RO2) to develop a novel distributed MPC-based algorithm that improves inter-vehicle spacing and tracking performance while generalizing beyond predefined reference trajectories; and (RO3) to prepare the algorithm for validation on a real-world robotic testbed.

The primary contributions of this work include: (C1) successful reproduction of the distributed baseline using open-source tools and realistic vehicle modelling; (C2) development of two enhanced distributed algorithms, Distributed Model Predictive Contouring Control with Relaxed Collision Avoidance (DMPC-RCA) and DMPC-RCA with consensus (DMPC-RCA-C), that demonstrate superior performance in tracking accuracy and safety margins; and (C3) integration of these algorithms with the DART hardware platform for future experimental validation. The repository of these approaches can be found in https://github.com/HubertVisser/multi-vehicle-coordination-algorithm.git

Performance is evaluated in two representative scenarios, a merging situation and a T-junction, with the centralised NMPC approach serving as the performance benchmark. To evaluate the performance of the proposed designs, three key metrics are used: accumulated tracking cost, computation time, and minimum inter-agent distance. The results show that both proposed methods achieve tracking costs comparable to the centralised controller, while significantly outperforming the distributed baseline method. Notably, the inclusion of a consensus term yields no substantial improvement in performance over the non-consensus version.

To conclude, the proposed approaches offer strong potential for scalable, safe, and efficient multi-agent motion planning, moving one step closer to the deployment of fully cooperative autonomous driving on public roads.

Github repository for the Master Thesis - https://github.com/HubertVisser/multi-vehicle-coordination-algorithm.git ... Read more

This thesis proposes a study towards the application of imitation learning (IL) algorithms Action Chunking Transformer (ACT) and diffusion policy as an autonomous surface vessel (ASV) path planner in complex marine environments. Rationale for conduct- ing this research are the ubiquitous limitations regarding fine tuning cost- and or reward functions of conventional state of the art algorithms for ASV navigation in complex environments. In this study we trained both algorithm types on data sources collected from a basic 2D-grid sim- ulator and a more realistic GAZEBO simulator. Subse- quently we evaluated both algorithm’s performances in each respective simulator in terms of the success rate for a standard navigation task. We found relatively high suc- cess rates for the 2D-grid simulator (0.98 for ACT and 0.53 for diffusion policy). For the GAZEBO simulator, we found poor performance for ACT (0.0 success rate) and for diffusion policy we could not establish the perfor- mance due to hardware limitations. For future work, the capabilities of both models could be investigated further by trying to bridge the gap between the simple 2D simula- tor and the GAZEBO simulator. Mainly the effect of task complexity and the quality and quantity of data used for training the models on the performances of the models can be investigated in these future work studies. ... Read more

UAVs increasingly require GNSS-independent positioning for operation in contested or infrastructure-denied environments. This paper presents a vision-based celestial navigation system with automatic adaptation to light pollution through dynamic star catalog selection. The algorithm employs DDR pattern matching with novel polar-star rejection and consensus-driven magnitude refinement to robustly identify observable stars under varying environmental conditions. Evaluation on 200 synthetic night-sky images demonstrates substantially improved star identification robustness compared to fixed-catalog baselines, achieving 71.5% recall at visual magnitude 7 (Bortle 3) and maintaining non-zero performance under severe light pollution (27.6% recall at magnitude 5.0 and 4.5% at magnitude 4.5), where the baseline fails entirely. Across higher limiting magnitudes (6.5–8.0), the adaptive method consistently attains 71.5–82.5% recall. Including misidentifications, the end-to-end system achieves a median geolocation error of 6.80~km, supporting coarse global localization, GNSS integrity monitoring, and long-duration drift bounding in GNSS-denied environments. These results indicate that adaptive catalog selection significantly extends the operational envelope of celestial navigation into light-polluted conditions previously considered infeasible. ... Read more

The rapid increase in drone threats has created an urgent need for practical counter-drone systems for safety and defence purposes. Interceptor drones represent a promising and cost-effective solution for removing unwanted aerial vehicles. However, classical guidance laws such as Proportional Navigation (PN) are not well adapted to highly agile targets under realistic perception constraints. In parallel, deep reinforcement learning (DRL) has demonstrated exceptional performance in high-speed quadcopter control tasks, motivating its consideration for interception applications. A major challenge for such learning-based controllers lies in sim-to-real transfer. This work addresses this challenge through extensive domain randomization and explicit modelling of realistic perception constraints, including sensor noise, limited field of view, sensing ranges, and sensor update rates. A framework is proposed that trains Single Rotor Thrust (SRT) DRL policies for quadcopters and evaluates them against a classical PN implementation within a physics-based simulator. The resulting controllers generalise across platform variations and outperform classical guidance under modelled perception limitations, demonstrating improved robustness and transferability.

Github Repository of the code used for the thesis: https://github.com/maximecapelle/quadintercept_drl ... Read more

Mechanical tracking systems offer potential advantages over optical and electromagnetic alternatives for surgical navigation, including freedom from line-of-sight constraints, immunity to metallic interference, and high-frequency position feedback. However, passive mechanical tracker arms lack the joint locking capability required for traditional laser tracker calibration, and existing artefact-based methods fail to provide the complete six-degree-of-freedom constraints necessary for accurate kinematic parameter identification. This thesis presents a novel artefact-based calibration methodology specifically designed for passive mechanical tracking arms used in surgical navigation applications. The methodology employs a laser-certified steel calibration artefact featuring 15 asymmetric pillars that provide complete 6-DoF constraint through single contact points. Unlike traditional sphere-based methods that only constrain position, the asymmetric plug design mechanically prevents rotation, enabling simultaneous identification of all kinematic parameters without sequential joint locking. The artefact was precision-machined and certified using a Leica AT960 laser tracker to ±0.001 mm uncertainty, providing metrological traceability one order of magnitude better than the target accuracy. Applied to a custombuilt six-degree-of-freedom Surgical Tracker Arm (SUTA), the calibration methodology achieved 1.01 mm RMS accuracy across 525 independent validation measurements, representing an 81.5% improvement from the 5.45 mm RMS baseline configuration. Dataset optimization analysis revealed that six measurement positions achieve near-optimal accuracy (1.09 mm RMS) while maintaining calibration efficiency, with marginal improvements beyond this point. The research identified mechanical interface compliance as the dominant error source, with clearance in the end-effector-pillar interface contributing approximately 0.5 mm to the total error. While the achieved accuracy does not yet match the 0.3-0.5 mm performance of state-of-the-art optical systems, the methodology successfully demonstrates that passive mechanical trackers can be calibrated to millimeter-level precision with a calibration artefact. The approach establishes a practical framework for calibrating passive tracking systems and identifies clear paths toward sub-millimeter accuracy through targeted hardware improvements, particularly in interface design and consistent seating mechanisms. ... Read more

This thesis focuses on enabling safe and reliable navigation for Autonomous Surface Vessels (ASVs) operating in complex and mixed-traffic maritime environments. It presents a suite of motion planning and control algorithms that ensure fault tolerance and compliance with maritime traffic rules, even under uncertainty and component failures. Key contributions include a Model Predictive Contouring Control (MPCC) method that formalizes COLREGs compliance, a model-based fault diagnosis framework using residual analysis, a robust Set-Membership Estimation (SME) approach for fault parameter identification, and a Robust Adaptive Model Predictive Control (RAMPC) scheme that integrates fault information into trajectory optimization. Validated through extensive simulations and implemented in ROS, the proposed framework demonstrates robust performance in dynamic and uncertain conditions, laying the groundwork for real-world deployment of autonomous maritime systems. ... Read more

Reinforcement learning has emerged as a promising approach for enabling robots to learn from interactions with their environments, without relying on predefined behaviors. However, robots face significant challenges when learning directly from real-world interactions. Real-world learning is time-consuming and resource-intensive, often requiring extensive data collection over long periods. Additionally, the risks involved in trial-and-error learning in physical settings are high, as faulty policies can lead to safety issues or system damage. Simulations offer a safer and more efficient alternative, allowing robots to learn in simulated environments at faster-than-real-time speeds. Despite these benefits, simulations often serve as imperfect approximations of reality. As a result, robots may learn behaviors that exploit simulation-specific quirks, which may not perform well in real-world settings, creating difficulties in transferring learned behaviors from simulation to real environments—a challenge known as the sim-to-real gap. Several factors contribute to the sim-to-real gap, such as unmodeled physical phenomena like friction and deformation, and the asynchronous nature of real-world systems that simulations often fail to capture accurately. Additionally, using separate software stacks for simulation and deployment can unintentionally lead to discrepancies. Finally, simulating at faster-than-real-time speeds with asynchronous frameworks that distribute computation across multiple cores may also introduce inaccuracies without proper synchronization.

This thesis focuses on improving simulation tools and methodologies to enhance the efficiency and effectiveness of learning-based approaches in robotics. The work addresses key trade-offs between flexibility, speed, and accuracy in robotic simulations, which are critical for successfully transferring learned policies from simulation to real-world environments. Additionally, it introduces a strategy to improve resilience, ensuring that learned behaviors are robust to irrelevant and unknown dynamics.
By tackling these challenges, this thesis provides insights into the design of effective robotic simulators and presents contributions that help bridge the gap between simulated and real-world robotic learning. ... Read more

Public safety and emergency response agencies increasingly consider the deployment of mobile robots as mounting climate-related disasters and security challenges place human personnel at higher risk and stress. Mobile robots, such as drones, are a promising strategy to respond to these challenges: They can navigate difficult, hazardous terrain, gather real-time situational data, and conduct search or reconnaissance tasks without putting humans at direct risk. However, the currently practiced teleoperation of robots is challenging for such complex missions since the simultaneous navigation, situation assessment, and search tasks can overload human cognitive abilities. Therefore, autonomous planning and decision-making algorithms are required to enable robots to explore and search unknown environments for targets such as missing persons or hazardous materials.

Moving towards this goal, this thesis addresses two core problems. First, local motion planning must carefully account for information gained from sensor observations as well as collision avoidance and the robot’s dynamics while moving through cluttered, unknown areas. Second, global exploration planning must strategically select where in the environment to explore to find the target quickly - especially when the environment is large or complex. Given that human operators often possess semantic knowledge about likely target locations, we hypothesize that incorporating such guidance by observed semantic features (e.g., object or room types) into the exploration planning is crucial for time-efficient autonomous search. We address these two core problems by making the following contributions.

The first contribution of the thesis is an informative local motion planning approach
that generates safe, collision-free trajectories around obstacles while minimizing uncertainty about the target locations. The critical challenge is to achieve computationally efficient planning of trajectories that maximize information gain under the robot’s kinodynamic constraints. In the proposed approach, a model predictive control (MPC) motion planner is guided by a learned viewpoint policy. The policy is trained via deep reinforcement learning (DRL) to maximize long-term information gain by providing a local subgoal to the MPC. The MPC follows the subgoal and ensures that the motion plan remains feasible and collision-free. Therefore, the robot can rapidly replan safe and informative local trajectories online. Simulation experiments demonstrate that the method achieves competitive performance in locating targets compared to a computationally expensive state-of-the-art planner using Monte Carlo Tree Search (MCTS), while allowing for significantly faster execution and replanning.

While local informative planning is crucial for exploring cluttered spaces, it often be-
haves myopically and inefficiently with respect to large and complex environments. Therefore, the second contribution introduces a global target search planner that balances directed search towards semantically promising areas with complete search space coverage. This planner extends the idea of frontier exploration - focusing observations on the boundaries between explored and unexplored regions - to target search, where different frontiers are assigned a semantic priority. This priority represents the semantic relationships between the target and nearby objects. To minimize target search time, the target search planner schedules high-priority frontiers earlier by solving a custom combinatorial optimization problem to determine the visitation order. By integrating coverage gains into the frontier priorities, the planner ensures that the robot explores the environment efficiently while focusing on semantically relevant areas. We demonstrate this approach in two studies outlined below.

Large, high-quality datasets for learning target-specific semantic relationships are
scarce in many real-world scenarios, especially in search and rescue. The third contribution addresses this limitation by proposing a method to learn semantic priority models from expert feedback. Rather than collecting massive amounts of labeled data, the approach exploits an expert operator’s sparse guidance inputs in a few target search scenarios. This expert guidance selects a frontier to explore next, which is stored in a training dataset together with the frontier’s semantic features. An expert model is then trained to approximate a priority function that predicts how relevant each frontier is for the expert. By incorporating this learned priority function into the global target search planner, the robot can autonomously prioritize semantically relevant areas according to the expert’s semantic knowledge. Experiments show that using a small number of expert demonstrations is sufficient for the robot to significantly improve its search efficiency and reduce travel distance until the target is found.

Lastly, the thesis extends semantic target search to three-dimensional environments
by integrating it into a 3D planning pipeline for micro aerial vehicles (MAV). The pipeline first detects objects in the environment using onboard vision and associates them with priority values computed from pre-trained large language model (LLM) embeddings. These priorities are then propagated into frontiers in a 3D voxel map, indicating frontier regions that are most likely to contain the target. This enables the evaluation of frontier viewpoints for their information gain that accounts for both semantic priority and volumetric coverage. The viewpoint gains are then used in the combinatorial target search planner to prioritize the viewpoints that most likely lead to the target while ensuring efficient coverage of the environment. By integrating the MAV’s kinodynamic constraints into the planning costs, the system ensures smooth, feasible trajectories in real-time. Simulation studies reveal that semantically guided exploration leads to faster and more reliable target discovery than different purely coverage-based exploration baselines. Experiments with a real MAV in the lab confirm the approach’s ability to autonomously navigate an MAV through complex 3D environments to a target, exploiting semantic cues, maximizing
coverage, and avoiding collisions.

In summary, this thesis demonstrates how planning and learning techniques can be
combined for autonomous target search and exploration. These techniques enable mobile robots to navigate unknown environments efficiently and safely while searching for targets and collecting required information. Crucially, our proposed method for semantically guided frontier planning bridges the gap between recent learning-based navigation approaches and established planning-based approaches suitable for real-world robotic systems. By integrating semantic knowledge into robotic exploration, the proposed methods can reduce human operator cognitive load and, therefore, facilitate robot deployment in scenarios such as search and rescue or reconnaissance missions. ... Read more

This thesis presents a COLREG-compliant collision avoidance and detection algorithm for the safe navigation of autonomous surface vessels. The method builds upon the Velocity Obstacle algorithm and implements the concept of the ship domain and the ship arena into the framework. The International Regulations for Preventing Collision at Sea – the COLREGs – are analyzed individually and incorporated in the designed collision avoidance algorithm. The COLREGs were used to determine the preferred passing side and optimized motion for every possible encounter situation with an obstacle vessel. The ship domain and arena help to perform proactive alterations when required, improving on the apparent shortcomings of the VO algorithm. This framework leads to a more proactive collision avoidance algorithm that guarantees COLREG compliance and safe navigation in mixed-traffic environments. The robustness and wide adaptability of the developed algorithm are validated in different encounter situations, ranging from vessel-to-vessel batch simulations to complex, multi-vessel scenarios. ... Read more

This work introduces a novel training strategy for Gaussian Process (GP) models aimed at improving their predictive accuracy and uncertainty quantification capabilities over extended prediction horizons. This improvement is highly relevant for applications in model predictive control (MPC) in the autonomous driving domain. Learning-based MPC strategies typically rely on standard physics-based models augmented with GP models to account for residual nonlinearities and uncertainties not captured by the former. Nonetheless, these conventional approaches often struggle with long-term prediction accuracy, especially when faced with out-of-distribution scenarios, a phenomenon where the model encounters data points that are significantly divergent from the training set. To address these challenges, a multi-step Gaussian process training framework is proposed. This framework yields a GP model capable of making accurate long-term predictions, i.e. a multi-step Gaussian Process (MSGP) model. It achieves this by integrating the simulation of future dynamics into the training process, allowing for the model’s kernel parameters to be tuned toward long-term dynamics. As a result, the MSGP model not only demonstrated the ability to make more stable and accurate long-term dynamic predictions but also with greater confidence. The efficacy of the multistep training framework is shown by the significant improvements in long-horizon dynamics predictions by the MSGP model, achieving an average 19% reduction in mean error and a 90% reduction in variance compared to the standard GP model. Moreover, the efficacy of the MSGP model is further confirmed through its application in a Multi-Step Gaussian Process-based Model Predictive Contouring Controller (MSGP-MPCC), which outperforms a traditional GP-based MPCC (GP-MPCC) baseline controller in lap time and reliability, achieving a 100% success rate in completing laps across ten consecutive simulations without crashing. ... Read more

Monitoring wildfires using multiple unmanned aerial vehicles (UAVs) is essential for timely intervention and management of the fire while minimising risks to human lives. A research gap was identified for practical UAV solutions integrating critical features, such as localisation, fire safety and battery management. This thesis presents a novel rule-based algorithm designed for effective wildfire monitoring using cooperative UAVs. A simulation environment with a wildfire spread model was developed to test and evaluate various strategies under dynamic conditions. The proposed algorithm operates in two phases: first, locating the fire and then monitoring its progression. Key features include a heat avoidance mechanism to maintain UAV safety and recharging schedules to maximise operational uptime. A fire detection map is centrally created using the infrared data from the UAVs. The convex hull around the fire serves as the basis for path planning to track the fire perimeter. The best strategy is determined and optimised through a series of experiments in the simulation environment. The overall best-performing algorithm makes navigational decisions based on the importance of different sections of the fire perimeter. The importance of the perimeter is constantly updated based on the fire spread, visit times, and distances of the UAVs. The assignment of various UAV tasks was reviewed to enhance UAV cooperation, which showed performance benefits across different scenarios. Other key findings
include the recommendation of a small fleet size of up to six UAVs, with a flight speed of 10 m/s at an altitude of 100 meters, balancing costs, performance and safety. The algorithm demonstrated performance equal to state-of-the-art reinforcement learning techniques
while offering advantages in explainability. Additionally, the algorithm has been successfully validated in a lab environment, demonstrating its potential as a practical and cost-effective solution for wildfire monitoring. This work brings a fire monitoring system closer to real-world implementation and will possibly help fight wildfires effectively. ... Read more

Logistics and transportation can greatly benefit from the use of autonomous robots, such as self-driving vehicles. Robots can help to move goods or people without human supervision. One of the main components that enable autonomous navigation among humans is motion planning. Motion planning is responsible for computing a collision-free trajectory that moves the robot to its destination, based on the perceived information. The motion planner should be efficient, robust, and safe. This thesis contributes towards this goal by investigating the design of motion planning algorithms for autonomous navigation of mobile robots near humans.

Traditional motion planners for dynamic environments have two key limitations that this thesis aims to address. First, they assume that their model of dynamic obstacles (e.g., humans) is exactly correct, capturing it with a single deterministic prediction. In practice, the robot cannot observe human intentions and must account for its uncertainty about the human's future behavior. Second, motion planners usually compute a single trajectory around an obstacle as a result of previously taken decisions without exploring alternative options. They react slowly or even fail to find a solution when unpredicted changes make this path undesirable. This results in poor planning performance in dynamic environments.

The goal of this thesis is to develop motion planners that account for the uncertainty of human motion predictions and that are consistent and robust in their decision-making in order to deal with unpredicted changes in dynamic environments. To accomplish this goal, this thesis proposes two motion planning frameworks: scenario-based and topology-driven trajectory optimization.

The first contribution of this thesis is Scenario-based Model Predictive Contouring Control (S-MPCC), a real-time capable probabilistic planning framework that incorporates any uncertainty associated with the motion predictions of dynamic obstacles. Contrary to existing methods that only account for small variations around a single predicted trajectory (unimodal uncertainty), the proposed planner accounts for multiple possible trajectories (multi-modal uncertainty). The planner therefore safely accounts for several outcomes, for instance, to express that a pedestrian may or may not cross in front of the robot.

S-MPCC bounds the probability of collision in each time step with all obstacles through Chance-Constrained Optimization (CCO). The CCO is reformulated as an optimization without uncertainty by sampling trajectories from the predicted distribution, known as scenarios. Each scenario represents a possible position of all obstacles in one time step, and the planner avoids collisions with all scenarios. This Scenario Program (SP), through a tailored linearization, can be solved efficiently online. S-MPCC therefore plans probabilistic safe trajectories independent of the underlying distribution of the uncertainty.

S-MPCC considers the probability of collision separately for each time instance in the planned trajectory. The second contribution of this thesis, Safe Horizon Model Predictive Control (SH-MPC), builds on S-MPCC to constrain the joint probability of collision with all obstacles over the duration of the planned trajectory. Existing methods that separately constrain the probability of collision in each time step (temporal marginal) and with each obstacle (obstacle marginal) lead to overly cautious motion planning when safety constraints are enforced. SH-MPC formulates a single chance constraint to bound the overall probability of collision. This CCO is reformulated as an SP where each scenario represents a possible trajectory for all obstacles. To certify the joint probability of collision with the SP, the number of scenarios that affect the motion plan needs to be identified. SH-MPC estimates this quantity at a negligible computational cost during optimization. Consequently, SH-MPC plans trajectories in real-time under generic uncertainties that are less cautious than existing methods without compromising on safety.

The probabilistic safety of S-MPCC and SH-MPC is linked to the underlying accuracy of the prediction model of the obstacles that provide the scenarios. As a third contribution, a joint prediction and planning framework, Partitioned Scenario Replay (PSR), is proposed that replays past observations of human motion as scenarios for scenario-based planning. PSR does not fit a distribution on observed data but directly uses the data as empirical evidence of the underlying uncertainty and thereby provides a real-world safety guarantee.

A key limitation of the developed scenario-based planners and other optimization-based planners is that they locally refine an initial trajectory. This initial trajectory largely determines the quality of the final trajectory, while it does not consider other options. The fourth contribution of this thesis is Topology-driven Model Predictive Control (T-MPC) that concurrently optimizes trajectories, each attempting a different way to pass the obstacles. T-MPC is composed of a guidance planner and several parallel local planners. The guidance planner identifies guidance trajectories for several distinct maneuvers, relying on results from topology to distinguish trajectories. Each local planner is composed of an existing optimization-based planner (e.g., a scenario-based planner) and an additional set of constraints that are derived from one of the guidance trajectories. The guidance trajectories are optimized by the local planners in parallel, and the results are compared to determine which trajectory gets executed. T-MPC is faster, more consistent, and safer than several state-of-the-art planners. Contrary to similar existing work, it does not rely on an explicit lane structure and therefore enables both urban driving and mobile robotic applications.

The motion planners developed in this thesis are extensively validated in simulation and in experiments with a small-scale mobile robot and a full-scale self-driving vehicle navigating among pedestrians. The robot-agnostic implementation of the proposed planners that were developed for this thesis is available open source. ... Read more

This thesis presents a comprehensive approach to integrating a trajectory planner and follower for autonomous vehicles (AVs) using model predictive contouring control (MPCC). The planner generates collision-free trajectories with a kinematic bicycle model, while the follower tracks them using a dynamic bicycle model with a smaller integration step size and higher update frequency. The hierarchical architecture allows for a long planning horizon and a fast control loop. Mismatches between the planner and follower can result in tracking errors and conservative trajectories. To address this, a feedback-hierarchical interface is proposed, feeding back the mismatch error from follower to planner. Obstacles are then inflated with this error, minimizing harmful deviations and collision risks. The paper validates the Local Motion Planner using a simulator with a dynamic bicycle model, testing different follower-solver settings in urban scenarios. The results show a reduction in collision rate of 23\% compared to a single-layer MPCC planner, with similar levels of lateral error and task duration. ... Read more