We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relaxed bipartite matching step generates high-quality schedules with feasibility guarantees. We explicitly model robot and task positions, task durations, and robots’ remaining processing times, enabling advanced temporal and spatial reasoning and generalization to environments with different spatiotemporal distributions compared to training. Trained on optimally solved small-scale instances, our method can scale to larger task sets and team sizes. Sadcher outperforms other learning-based and heuristic baselines on randomized, unseen prob-
lems for small and medium-sized teams with computation times suitable for real-time operation. Wealso explore sampling-based variants and evaluate scalability across robot and task counts. To address performance limitations identified for large-scale problem instances, we experiment with using Reinforcement Learning (RL) to fine-tune the imitation-learned model. Both discrete and continuous RL formulations are explored, leveraging Proximal Policy Optimization (PPO). This allows for training on larger problem instances, for which optimal solutions for IL are infeasible to obtain. Our RL experiments provide insights into the comparative advantages and trade-offs of IL and RL methods. In addition, we release our dataset of 250,000 optimal schedules to facilitate future research. We include a detailed description of the instance generation and the Mixed-Integer Linear Programming (MILP) formulation used to solve them optimally. Furthermore, this thesis contributes a lightweight
simulation environment with visualization tools for benchmarking different task assignment algorithms.

In the context of lunar exploration missions, autonomous navigation is essential to limit reliance on human intervention, which is hindered by significant communication delays and limited bandwidth. However, due to constrained on-board computational resources, complex robot-soil interactions, and the inherent challenges of full autonomy, most lunar rovers are currently limited to speeds of around 10cm/s. In this work, we investigate the use of Model Predictive Path Integral Control (MPPI) for high-speed navigation on uneven and cluttered lunar terrain. To this end, we develop an MPPI controller using the WARP framework, allowing to easily leverage the inherent parallelisation capabilities of GPUs. Experiments were conducted with a simulated Husky rover navigating realistic lunar terrain generated from NASA data, demonstrating the effectiveness of our motion planner. Since most MPPI implementations assume flat ground when sampling trajectories, this simplification can introduce inaccuracies, resulting in suboptimal, and sometimes even risky paths. To address this limitation, we propose a lightweight mathematical method for 3D trajectory projection, and evaluate its performance against the standard 2D MPPI approach. Overall, simulation results show that our 3D-enhanced MPPI significantly outperforms its 2D counterpart in terms of both traversal speed and obstacle avoidance on sloped, rock-scattered terrain. On flat ground, however, the traditional 2D MPPI still exhibits a high level of robustness. These findings suggest that an adaptive strategy, dynamically switching between 2D and 3D trajectory projection based on local terrain features, could offer a valuable trade-off between performance and power efficiency.

This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-art controllers that utilize a centralized scheme, our policy does not require global states, inter-MAV communications, nor neighboring MAV information. Instead, agents communicate implicitly through load pose observations alone, which enables high scalability and flexibility. It also significantly reduces computing costs during inference time, enabling onboard deployment of the policy. In addition, we introduce a new action space design for the MAVs using linear acceleration and body rates. This choice, combined with a robust low-level controller, enables reliable sim-to-real transfer despite significant uncertainties caused by cable tension during dynamic 3D motion. We validate our method in various real-world experiments, including full-pose control under load model uncertainties, showing setpoint tracking performance comparable to the state-of-the-art centralized method. We also demonstrate cooperation amongst agents with heterogeneous control policies, and robustness to the complete in-flight loss of one MAV. Videos of experiments: https://autonomousrobots.nl/paper_websites/aerial-manipulation-marl

Autonomous planetary rovers require obstacle detection capabilities to navigate hazardous terrain without Earth-based intervention, yet currently deployed methods are limited. This research develops and validates a complete deep learning system for autonomous rock detection on a resource-constrained planetary rover.
We present a lightweight MobileNetV2-based U-Net architecture with dual attention mechanisms, optimized for edge deployment with only 0.31 million parameters. A new dataset MarsTanYard was created via a semi-automated dataset creation pipeline, enabling efficient annotation of Mars-analogue terrain imagery. Our deep learning network was trained on this dataset and integrated with a ROS2 navigation stack through a modular architecture that transforms segmentation masks into 2D occupancy grids that can be used for rover path planning. Our network achieves a 77% intersection-over-union accuracy for rock segmentation, and physical validation on a testing rover in a Mars analogue environment demonstrates a 94% detection rate for large rocks at close-range. An inference time of 4.49ms was achieved on the target rover hardware using model optimization techniques. The system maintains reliable operation across varying lighting conditions with less than 15% performance degradation.
Results show theoretical collision probability of 7.8 × 10^-7 per rock encounter, enabling months of autonomous operation for typical planetary missions. This work provides an end-to-end validation of the deep learning obstacle detection system, establishing a foundation for enhanced rover autonomy in future Mars exploration missions.

Autonomous UAV swarms have great potential for applications such as search‐and‐rescue, wildfire monitoring, and environmental mapping, but rapid and reliable coverage of large, obstacle‐filled areas remains challenging. In this paper, we ask: How can decentralised multi-UAV systems be optimised for large-scale priority-aware exploration? To answer this, we present the Fast LIDAR-based Autonomous Multi-UAV Explorer (FLAME), a hierarchical framework for efficient swarm exploration with long‐range, 360° LIDAR sensors. Locally, a dual‐mode exploration planner alternates between high‐speed straight‐line travel and frontier-cost‐based exploration. Globally, each UAV’s mission is optimised to visit user‐defined high‐priority regions early while minimising total mission time, enabling decentralised coordination with minimal duplicate exploration. FLAME also integrates battery‐constrained multi‐mission planning, dividing large tasks into multiple battery‐feasible missions with timely returns to the ground station for recharging or battery swaps. In simulation, FLAME achieves 36% lower mission time compared to state-of-the-art. A high-fidelity simulation confirms the framework’s ability to effectively prioritise user-assigned regions while adhering to operational constraints.

Autonomous drone racing presents a unique challenge that requires both high-speed motion planning and strategic decision-making in a multi-agent setting. Prior work has primarily relied on model predictive control (MPC) methods that treat opponents as dynamic obstacles, limiting their ability to model strategic interactions. In this work, we formulate drone racing as a dynamic game and introduce game-theoretic planning methods that compute open-loop Nash equilibria, incorporate blocking strategies, and accelerate decision-making using learning-based techniques. These methods explicitly model opponent behavior, allowing drones to anticipate and react strategically in high-speed racing scenarios. To assess the effectiveness of our approach, we conduct a large-scale head-to-head tournament against MPC-based planners, demonstrating that interaction-aware planning enables more effective overtaking and defensive strategies, leading to a higher wining rate. However, computational delays in high-speed decision-making can limit performance, highlighting the need for efficient techniques that balance real-time feasibility with strategic adaptability. Our results show that learning-based acceleration significantly improves decision-making speed while preserving competitive advantages. Finally, high-fidelity simulations and real-world drone racing experiments validate the feasibility of these methods, confirming their ability to generate reliable and competitive strategies under practical racing conditions.

Unmanned Surface Vessels (USVs) face significant control challenges due to uncertain environmental disturbances, such as waves and currents. This thesis proposes a trajectory tracking controller based on Active Disturbance Rejection Control (ADRC) implemented on the DUS V2500, a seagoing USV developed by Demcon Unmanned Systems. A custom simulation incorporating realistic wave, wind, and current disturbances is developed to validate the performance of the controller. Simulated experiments are supported by further validation through field tests in the harbour of Scheveningen, the Netherlands, and at sea. Simulation results demonstrate that ADRC significantly reduces cross-track error across all tested conditions compared to a baseline PID controller, but increases control effort and energy consumption. Field trials in Scheveningen, the Netherlands, confirm a reduction in cross-track error, while revealing a further increase in energy consumption during sea trials compared to the baseline controller. Modifications to the controller are proposed to improve its efficiency, and the impacts on tracking performance are discussed.

To safely and efficiently solve motion planning problems in multi-agent settings, most approaches attempt to solve a joint optimization that explicitly accounts for the responses triggered in other
agents. This often results in solutions with an exponential computational complexity, making these methods intractable for complex scenarios with many agents. While sequential predict-and-plan approaches are more scalable, they tend to perform poorly in highly interactive environments. This paper proposes a method to improve the interactive capabilities of sequential predict-and-plan methods in multi-agent navigation problems by introducing predictability as an optimization objective. We interpret predictability through the use of general prediction models, by allowing agents to predict themselves and estimate how they align with these external predictions. We formally introduce this behavior through the free-energy of the system, which reduces (under appropriate bounds) to the Kullback-Leibler divergence between plan and prediction, and use this as a penalty for unpredictable trajectories. The proposed interpretation of predictability allows agents to more robustly leverage prediction models, and fosters a ‘soft social convention' that accelerates agreement on coordination strategies without the need of explicit high level control or communication. We show how this predictability-aware planning leads to lower-cost trajectories and reduces planning effort in a set of multi-robot problems, including autonomous driving experiments with human driver data, where we show that the benefits of considering predictability apply even when only the ego-agent uses this strategy.

Mobile manipulators, which integrate a robotic arm on a mobile base, are increasingly being explored and deployed in sectors such as healthcare, logistics, and aerospace. While motion planning for these systems has been studied in single-agent scenarios, the use of multiple robots to enhance efficiency and accelerate task completion in multi-agent settings remains largely unexplored, particularly in real-world environments. Extending motion planning to multi-mobile manipulators introduces challenges in real-time performance, collision avoidance, and coordination. To address these, this thesis proposes a decentralized Model Predictive Control (MPC) framework with a double integrator as dynamic model, denoted as MPC-d, tailored for multi-mobile manipulators operating in shared workspaces. It integrates optimization-based planning with robust state estimation, ensuring effective collision avoidance. Furthermore, a prioritized heuristic is introduced, leveraging the prediction horizon of MPC to resolve potential livelocks. The framework is validated through simulations and real-world experiments. Simulations compare MPC-d with MPC using a triple-integrator model (MPC-t) and a state-of-the-art geometric planner, called Geometric Fabrics (GF). Results demonstrate that MPC-d achieves comparable task success rates and collision avoidance compared to GF in pick-and-place scenarios while requiring less computation time than MPC-t. Real-world experiments confirm the framework’s viability, showcasing effective collision avoidance, enhanced efficiency from the prioritized heuristic, and consistency with simulation outcomes. Although MPC-d incurs higher computational costs than reactive geometric methods, it provides reliable performance and motion prediction of other agents in multi-agent settings.

The application of multi-robot systems has gained popularity in recent years. Multi-robot systems show great potential in scaling up robotic applications in surveillance, monitoring, and exploration. Although single robots can already be used to automatize search and rescue, and surveillance tasks, their capability to solve their given task is still dependent on the region of interest size. For example, in expansive environments, a single robot’s ability to cover or surveil the entire area effectively diminishes, resulting in information gaps. To this end, this thesis aims to improve multi-robot coordination and navigation in active perception tasks, e.g. exploration and surveillance. The multi-robot coordination problem aims to determine when and with whom multiple robots should exchange information to avoid collisions while navigating to their goal. The active perception problem involves guiding a sensing robot to positions rich in task-relevant information. The primary objective of this dissertation is to provide algorithmic contributions, specifically focusing on the obtention of robust policies that can adapt and scale to fleets and tasks of varying sizes. To this end, this thesis leverages the combination of high-level policies, learned through Reinforcement Learning, with robust low-level optimal control policies. Firstly, the communication-based coordination problem for decentralized multi-robot systems is formally defined and modeled as a Markov Decision Process. This thesis then proposes a novel communication policy for decentralized multi-robot systems, improving coordination and collision avoidance. The proposed policy, learned via Reinforcement Learning, allows robots to selectively communicate, requesting trajectory plans from potential risks while assuming constant velocities for others. This utilizes an attention-based neural network architecture for scalability, integrated with Non-Linear Model Predictive Control for safe and robust motion planning. When tested with 12 robots, it reduced communications compared to alternatives, maintaining safety. Scalable and robust, it performed well with different team sizes and in the presence of observation noise. Real-world tests on quadrotors confirmed its practical applicability. The Active Perception problem represents the second major challenge addressed in this dissertation. Specifically, this thesis addresses the challenge of using a drone to collect semantic information for classifying multiple moving targets, focusing on computing control inputs for optimal viewpoints. This task is complicated by the variable amount of targets to classify in the region of interest, and the use of a ”black-box" classifier, like a deep learning neural network, which lacks clear analytical relationships between viewpoints and outputs. This thesis proposes an attention-based architecture, trained with Reinforcement Learning (RL), which determines the best viewpoints for the drone to gather evidence from multiple unclassified targets, considering their movement, orientation, and potential occlusions. A low-level MPC controller then guides the drone to these viewpoints. The approach outperforms various baselines and shows adaptability to new scenarios and scalability to numerous targets with varying movement dynamics. To conclude the thesis, the previous approach is extended to more realistic and larger environments where targets need to be localized, tracked, and then classified. This thesis introduces a novel decentralized hybrid multi-camera system designed for surveillance and monitoring applications. Traditional fixed camera networks suffer from blind spots and backlighting issues. A decentralized hybrid framework is proposed that integrates both static and mobile cameras to actively and dynamically enhance critical information gathering. All networked cameras collaborate to monitor and localize people in the environment by comparing their local information. The mobile camera is guided by a viewpoint control policy to maximize semantic information from observed targets. The framework was implemented in a photorealistic environment using Unreal Engine and enabled distributed communications through the Robot Operating System (ROS), bridging the gap between simulation and real-world applications. Results in large environments demonstrate the advantages of collaborative mobile cameras over static and individual setups both in target identification and tracking accuracy, respectively. In crowded scenarios, mobile cameras excel in avoiding occlusions and capturing desired viewpoints, improving the percentage of classified tracked targets compared to static setups. Qualitatively, mobile cameras provide superior target observation quality unmatched by the static framework. In summary, this thesis makes significant contributions that are validated through extensive evaluations in simulated photo-realistic environments and with commercial drones, demonstrating the potential for practical applications. Despite the progress, the thesis acknowledges the remaining challenges in deploying multi-robot systems in realworld perception tasks, especially when the policy is learned, and suggests directions for future research.

In urban environments like Amsterdam, where road congestion and stress on infrastructure are critical issues, the city’s waterways remain underutilized for transportation. Autonomous Surface Vessels (ASVs), making waterway transport cheaper and less labor intensive, present a potential solution by reducing transportation costs and alleviating road traffic. Although companies are developing ASVs, current solutions are mainly limited to larger, less congested waterways, with further innovation needed for denser urban areas.

The challenges in autonomous navigation for ASVs in urban canals stem from the complex nonlinear dynamics of vessels, which require long-term planning and rapid responses to environmental changes. Urban waterways are narrow and unstructured, with loosely defined navigation rules that can lead to discontinuities in the motion planner’s cost function. Typically, motion planners rely on predictions of other agents’ movements and plan around them, resulting in no interaction awareness. This approach can lead to the freezing robot problem in dense environments, where the ASV halts, deeming all the space unsafe. A local motion planner must address these challenges by ensuring long-horizon interaction-aware motions, rule adherence, and real-time planning.

An emerging approach in autonomous navigation is a sampling-based Model Predictive Control (MPC) strategy known as Model Predictive Path Integral control (MPPI). This algorithm approximates the optimal control sequence by sampling from a continuous input distribution. Thanks to its gradient-free nature, MPPI only requires collision checking to avoid collision and allows discontinuous cost functions. Additionally, its computation speed remains largely unaffected by the complexity of the robots’ nonlinear dynamics, enabling longer planning horizons. While this approach has grown in popularity in the motion planning community, its application in dynamic environments with multiple interacting agents remains relatively unexplored.

Building on the state-of-the-art in MPPI, this thesis first introduces an Interaction-Aware Model Predictive Path Integral (IA-MPPI) controller tailored for motion planning in crowded urban canals. While conventional planners passively react to other agents, IA-MPPI actively predicts and plans cooperatively in real-time, addressing the freezing robot problem and handling discontinuous navigation rules. The decentralized, communication-free architecture assumes agent cooperation and employs a two-stage sample evaluation to enhance efficiency. This approach manages nonlinear dynamics, exact obstacle shapes, and longer planning horizons, demonstrating robustness and efficiency compared to state-of-the-art MPC in multi-agent environments.

While IA-MPPI uses a constant velocity model for predicting other agents’ hidden goals, Chapter 4 of this thesis introduces a learning-based trajectory prediction model trained on realistic artificial data to improve accuracy in crowded environments. By extracting the agents’ goals from predicted trajectories and integrating this information into the IA-MPPI controller, the planner’s performance increases significantly in environments with tight interactions. Moreover, this approach outperforms treating the predicted trajectory as occupied space, leading to safer navigation in dense urban canals.

One of MPPI’s main drawbacks is that it struggles in highly dynamic environments because it usually only samples around a previous plan. When rapid changes occur, such as strong disturbances or an obstacle unexpectedly cutting off the path, the previous plan becomes invalid, and all the generated samples may lead to collisions. To address this, Chapter 5 introduces Biased-MPPI, incorporating multiple classic and learning-based ancillary controllers into the sampling distribution. While the mathematical derivations show that this introduces a bias, this significantly improves reactivity, safety, and robustness to local minima. Additionally, this approach reduces the required samples, making the controller more efficient regarding computational demand.

Lastly, Chapter 6 leverages the gradient-free nature of MPPI and applies it to a different domain involving contact-rich manipulation tasks, which are notoriously difficult for model-based planning. With a parallelizable GPU-based physics engine like IsaacGym, MPPI can efficiently roll out hundreds of input sequences in parallel, faster than in real-time, to approximate optimal control. This approach eliminates the need for explicit modeling of contact dynamics by exploiting the models embedded in the simulator, making it particularly suited for manipulation tasks like pushing and picking with both prehensile and non-prehensile manipulators. Experiments with real robots demonstrate the effectiveness of this method in handling discontinuous, contact-heavy environments.

Overall, this thesis explores key aspects of motion planning, particularly for ASVs, with extensions to ground robots and mobile manipulators. It advances the MPPI framework for autonomous navigation by adapting it for multi-agent systems and introducing biases through classic and learning-based ancillary controllers. Additionally, the thesis compares MPPI to MPC in navigation experiments and to optimization fabrics in manipulation tasks. The work presented promotes the use of MPPI, especially in domains where complex nonlinear dynamics and discontinuous costs complicate the use of real-time optimization-based MPC.

Autonomous motion planning requires the ability to safely reason about learned trajectory predictors, particularly in settings where an agent can influence other agents' behavior. These learned predictors are essential for anticipating the future states of uncontrollable agents, whose decision-making process can be difficult to model analytically. Thus, uncertainty quantification of these predictors is crucial for ensuring safe planning and control. In this work, we introduce a framework for interactive motion planning in unknown dynamic environments with probabilistic safety assurances. We adapt a model predictive controller (MPC) to distribution shifts in learned trajectory predictors when other agents react to the ego agent's plan. Our approach leverages tools from conformal prediction (CP) to detect when the other agent's behavior deviates from the training distribution and employs robust CP to quantify the uncertainty in trajectory predictions during these agent interactions. We propose a method for estimating interaction-induced distribution shifts during runtime and the Huber quantile for enhanced outlier detection. Using a KL divergence ambiguity set that upper bounds the distribution shift, our method constructs prediction regions with probabilistic assurances in the presence of distribution shifts caused by interactions with the ego agent. We evaluate our framework in interactive scenarios involving navigation around autonomous vehicles in the BITS simulator, demonstrating enhanced safety and reduced conservatism.

Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either focus on 2D semantic navigation in structured environments – which is less effective in complex 3D settings, or on robotic exploration in cluttered spaces – which often lacks the semantic reasoning needed for efficient target search. This thesis overcomes these limitations by proposing a novel framework that utilizes semantic reasoning to minimize target search and exploration time in unstructured environments using a MAV. Specifically, the open vocabulary inference capabilities of Large Language Models are employed to embed semantic relationships in segmentation images. An active perception pipeline is then developed to guide exploration toward semantically relevant regions of 3D space by biasing frontiers and selecting informative viewpoints. Finally, a combinatorial optimization problem is solved using these viewpoints to create a plan that balances information gain with time costs, facilitating rapid location of the target. Evaluations in complex simulation environments show that the proposed method consistently outperforms baselines by quickly finding the target while maintaining reasonable exploration times. Real-world experiments with a MAV further demonstrate the method's ability to handle practical constraints like limited battery life, small sensor range, and semantic uncertainty.

Human Mesh Recovery (HMR) frameworks predict a comprehensive 3D mesh of an observed human based on sensor measurements. The majority of these frameworks are purely image-based. Despite the richness of this data, image-based HMR frameworks are vulnerable to depth ambiguity, resulting in mesh inaccuracies in 3D space. Several HMR frameworks in the literature use LiDAR data to avoid this depth ambiguity. However, these frameworks are either only LiDAR-based, which limits performance due to LiDAR sparseness and limited training data, or they are model-free, making the resulting meshes vulnerable to artifacts and limited detail. Therefore, this work introduces SMPLify-3D, an optimization-based HMR framework that combines the richness of image data and the depth information within sparse LiDAR data to improve the 3D mesh inaccuracies of image-based HMR frameworks. The proposed framework consists of three main steps: 1) a body part visibility filter based on the 2D detected keypoints, 2) rough alignment between the mesh and the observed LiDAR point cloud using the ICP algorithm, and 3) an optimization scheme, inspired by SMPLify, that modifies the actual pose and shape of the mesh to improve both the 3D and image alignment. SMPLify-3D is versatile compared to other methods and outperforms image-based and SMPL-compatible LiDAR-based HMR frameworks by improving the Per-Vertex-Error (PVE) with 45% and 26% on the 3DPW and HumanM3 datasets respectively. Multiple quantitative experiments are conducted to show the effects of LiDAR noise and sparsity on the framework’s performance. Additionally, qualitative results illustrate how the proposed method achieves superior results on out-of-sample data recorded by a mobile robot. The source code for this work is available at: https://github.com/guidodumont/SMPLify-3D

Brachytherapy (BT) is an essential component in the curative treatment of cervical cancer. With commercial BT implant devices, called applicators, the radioactive sources can only be positioned in fixed intracavitary channels or in a fixed array of interstitial needles. Patient-tailored BT applicators containing individualised needle channels have the potential to enhance the delivered dose to the tumour while minimising tissue damage of the organs-at-risk (OARs). During the optimisation process of the needle channels, several constraints must be considered. The needle paths in the applicator are curvature-constrained, and the needles should not collide with each other or potentially perforate OARs. Furthermore, clinicians can impose additional constraints based on their preferences.

Existing approaches in literature for optimising needle placement in patient-tailored BT applicators for cervical cancer treatment have employed various algorithms. However, these approaches often rely on significant approximations. For instance, some assume that all needles are inserted in parallel or focus solely on optimising geometric coverage as opposed to optimising the dose distribution. Moreover, the few methods in literature incorporating needle path planning in the optimisation process require manually pre-specified dwell positions. To improve dose conformity and minimise the dependence on the clinician’s expertise and time, there is a need for the development of software that optimises the needle placement and paths without resorting to these severe assumptions.
This work proposes and validates a novel three-stage approach to generate a patient-tailored applicator configuration without resorting to fully geometric optimisation. First, a high-potential set of dwell positions is obtained by running a modified version of the dwell time optimisation software BiCycle (Erasmus Medical Centre, Rotterdam, the Netherlands) on a grid of possible dwell positions. This results in a resolution optimal treatment plan when not considering geometry and applicator constraints. The positions with the highest dwell times according to the resolution optimal treatment plan are selected in the high-potential set. Next, a weighted set cover problem is used iteratively to find a combination of feasible needle segments to cover all dwell points in the high-potential set at a minimum distance. Lastly, needle channels to steer the needle to these segments are simultaneously optimised to be of minimum curvature and mutually collision-free.

To evaluate our approach, a virtual configuration and planning study was performed in a cohort of 22 locally advanced cervical cancer patients previously treated with the Venezia (Elekta AB, Stockholm, Sweden). The resulting treatment plans of the clinically used configuration were compared with the resulting treatment plans of the proposed patient-tailored and grid configurations on clinically relevant dose-volume histogram parameters, dwell times, conformity index and number of interstitial needles. Statistical significance is assessed with Wilcoxon signed-rank tests.

The proposed workflow was demonstrated to be feasible, and for every patient, a configuration could be generated in clinically acceptable time. All treatment plans generated for the grid configuration, the patient-tailored configuration and the clinical configuration were acceptable following the EMBRACE ll aims. Planning aims, however, were met more frequently with both the grid configurations (145/151 instances) and the patient-tailored configurations (137/151 instances) in comparison with the clinically used configurations (119/151 instances). The treatment plans generated with the grid configurations obtained significantly better (p < 0.01) median normalised CTVIR D98 dose with respect to the clinical configurations. Moreover, with the grid configurations, there was a significant improvement in median normalised D2cm3 dose for the bladder (p < 0.001), rectum (p < 0.001), sigmoid (p < 0.01) and bowel (p < 0.001) compared to treatment plans obtained with the clinical configurations. The treatment plans generated with the patient-tailored configurations obtained comparable target doses, but median normalised D2cm3 doses for the bladder (p < 0.001), rectum (p < 0.01) and bowel (p < 0.01) were significantly better for the patient-tailored configurations compared to the clinical configurations.

The proposed automated patient-tailored BT source channel configuration planning method was demonstrated to be clinically feasible. The resulting treatment plans have dosimetric advantages over the treatment plans generated with the clinical applicator configuration. Improvements to the intracavitary dwell position placement are expected to further increase dose conformity.

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through Certified Learning (CL), which combines a learning-based controller with formal methods to provide safety guarantees. This thesis focuses on the CL based on Control Barrier Functions (CBFs), as CBFs have been widely used for safety-critical systems. However, it is non-trivial to design a CBF. Utilizing neural networks as CBFs has
shown great success, but it necessitates their certification as CBFs. In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space. To accelerate training, we further present a framework that embeds the verification scheme into the training loop to synthesize and verify a neural CBF simultaneously. In particular, we
employ the verification scheme to identify partitions of the state space that are not guaranteed to satisfy the CBF conditions and expand the training dataset by incorporating additional data from these partitions. The neural network is then optimized using the augmented dataset to meet the CBF conditions. We show that for a non-linear control-affine system, our framework can efficiently certify a neural network as a CBF and render a larger safe set than state-of-the-art neural CBF works. We further employ our learned neural CBF to derive a safe controller to illustrate the practical use of our framework.

Mobile manipulators, which combine a mobile platform with a robotic arm, are versatile robots that can be used for a variety of tasks like logistic pick-and-placing, manufacturing or assembly. Compliant control for mobile manipulators could improve the safety of the users sharing their workspace with these robots. The two general methods of compliant control, admittance control and impedance control, require force/torque sensors, which are often not available on low-cost or lightweight robots. This report presents an adaption of impedance control, including a strategy to compensate for joint friction, that can be used on current-controlled robots without the use of force/torque sensors. A calibration method is designed for the arm, that enables estimation of the actuator's current/torque ratios and frictions, used by the adapted impedance controller. Software is developed to use the controller on a combination of the Kinova GEN3 Lite arm and the Clearpath Dingo Omnidirectional base. Real-world experiments with the arm show that the calibration method is consistent and that the designed controller is compliant while also being able to track targets with five-millimeter precision when no interaction is present. Experiments with the complete mobile manipulator showcase two new modes, both suitable for interaction with a human. The first is a guidance mode where the user can control the robot using interaction with the arm. The second is a tracking mode where the mobile manipulator tracks a moving target while still being compliant.

Deep reinforcement learning presents a compelling approach for the exploration of cluttered 3D environments, offering a balance between fast computation and effective vision-based navigation. Yet, the use of 3D navigation for learning-based information gathering remains largely unexplored. Navigation in 3D space poses the challenge of having an increased state space but also provides possibilities due to the agent's increased mobility, so it is an interesting direction for research. Furthermore, current approaches to target mapping with 3D navigation do not consider cluttered environments, failing to address obstacle avoidance and occlusion handling.

This research introduces a novel deep reinforcement learning policy for vision-based information gathering with quadcopters, enabling efficient exploration of cluttered 3D environments. The core challenge is learning a time-efficient and collision-free exploration strategy, for which the policy design and the training procedure have been successfully developed. We formulate a target-searching task, where the goal is to reduce the agent's uncertainty about the target state. To achieve this goal, our method combines vision-based reasoning by deep reinforcement learning and probabilistic target mapping with an information-theoretic rewarding scheme to obtain a policy that makes informed exploration decisions.

Experiments comparing our method with a privileged greedy baseline show that in all tested environments, our policy achieves a significant outperformance. The results from our ablation study further validate our policy design, as every ablation performed results in worse exploration performance. Generally, our policy shows intelligent behaviour by effectively navigating through rooms and around obstacles. However, improvements can still be made, since failure cases where the agent gets stuck, can sporadically occur. Still, overall, the findings prove the feasibility of learning a 3D navigation policy for effective target mapping with quadcopters.

Unmanned aerial vehicles (UAVs), particularly quadcopters, are increasingly employed in diverse applications due to their manoeuvrability and affordability. However, their susceptibility to GPS jamming and the need for skilled operators limit their operational resilience. This paper presents a new design of an Image-Based Visual Servoing (IBVS) control system for autonomous quadcopter interception, utilizing only a monocular camera and an Inertial Measurement Unit (IMU). The system features a Multi-Axis PID Controller with acceleration limiting and a virtual plane projection method to decouple pitch and vertical motions, thereby enhancing interception accuracy and maintaining target visibility within the drone’s field of view (FOV). Furthermore, a perception module is designed to run Yolo-v8n and CSRT in parallel, followed by a Kalman filter, to deliver an accurate and robust representation of the target within the image, operating at 18 frames per second in Simulation-in-the-Loop (SITL) environments. Comprehensive SITL experiments and comparative analyses against recent IBVS algorithms demonstrate that the proposed proposed system achieves a 19% reduction in circular error probable (CEP) for static targets and superior performance in diverse scenarios involving moving targets. These findings validate the effectiveness of the proposed IBVS approach, offering a reliable and scalable solution for autonomous UAV interception in GPS-denied environments with potential applications in security, surveillance, and conflict zones.

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.