This report outlines the design and implementation of a subsystem within the automated e-nose prototype, which is based on a CMOS Pixelated Capacitive Sensor (PCS) array. The objective of the e-nose prototype is to detect the presence and identify the type of volatile organic compounds (VOCs). A potential use case is to accommodate greenhouses with these e-noses to detect infestations in plants.
The subsystem presented in this report is tasked with programming the FRDM-MCXN947 development board to enable and improve communication and control between the PCS array and the microcontroller unit (MCU). In addition, the development board is also explored to investigate the feasibility of replacing currently used external modules with on-board peripherals. To achieve this, the CTIMER and SCTIMER are analyzed and implemented with the MCUXpresso IDE. The results showed that both of these peripherals have difficulties in simultaneously generating clock signals of different frequencies required for the communication protocol between the sensor and the MCU. However, the SCTIMER is capable of generating a differential clock, currently produced by the AD9552 external clock, though with lower signal quality.

This thesis presents the design and implementation of a socket, a chamber and an automated control system for an electronic nose (e-nose) based on CMOS Pixelated Capacitive Sensor (PCS) array chip in a Quad Flat No-Leads (QFN) 5 mm x 5 mm chip package. The objective of this project is to enable repeatable and reliable detection of volatile organic compounds (VOCs) by ensuring a gas-tight sensing environment and minimizing human intervention in experimental procedures, while ensuring good electrical connections by the socket.

A socket was developed to ensure good electrical contact using an anisotropic conductive sheet, allowing the QFN-packaged PCS chip to be inserted and removed without the need for soldering. The socket interfaces with a tightly sealed chamber to prevent gas leakage (< 0.1 ml/min) and maintain a stable gas flow across the sensor. Both components were prototyped in Polyethylene Terephthalate Glycol (PETG) and therefore do not fulfill the final requirement for heat and chemical resistance yet.

To automate gas delivery, the system integrates Bronkhorst mass flow controllers and RVM industrial microfluidic rotary valves. A Python-based graphical user interface (GUI) was developed to schedule gas flow profiles, control valve positions and compute VOC concentrations using the Antoine equation and Dalton's Law of Partial Pressures. This enables dynamic delivery of programmable VOC concentration. Although the automated control system meets most mandatory requirements and all trade-off requirements, some limitations remain. These include unreliable serial communication with the valve during automated operation, even though manual operation through the GUI is executed reliably. This inconsistency is likely caused by timing or firmware-related issues.

This thesis addresses the development of a supporting electronics platform for a CMOS Pixelated Capacitive Sensor (PCS) array intended for high-sensitivity applications. The work focuses on enabling accurate extraction of capacitance changes at the sensor interface and translating them into signals suitable for digital processing. The design process emphasizes functional integration and signal integrity.

A custom analog front-end was developed, featuring a transimpedance amplifier (TIA) optimized for low input-referred noise and sufficient bandwidth to preserve pixel-level signal integrity. The complete analog signal path supports capacitive measurements with attofarad-level resolution and readout frequencies ranging from 1 to 100 MHz.

The main PCB integrates the readout circuitry with a central microcontroller (MCXN947), DAC-controlled programmable power supplies, and a variety of user interface connectors, all within a compact six-layer mixed-signal stackup. Particular attention was given to minimizing electromagnetic interference (EMI) and power supply noise through careful grounding, power segmentation, and layout strategies. Although the PCB theoretically satisfies most mandatory and trade-off requirements, including spatial and interface constraints, final verification of the noise performance remains pending due to fabrication lead times.

This work contains two systems created to raise abstraction for the Haskell-based HDL Clash.

A common tool in hardware design is the waveform viewer. Although Clash could already generate waveform files, these only contained binary representations of the values. Without translating these to Haskell values, they are difficult to interpret. Shockwaves was created to perform this translation. Unlike other typed waveform solutions, Shockwaves performs the translation fully in the Haskell runtime, and stores the results in lookup tables. This gives the programmer full control over the waveform representation of data. There are two methods of generating VCD files from Clash, and Shockwaves was designed to work with both. The system is fully functional for signals traced during direct simulation. The alternative approach of simulating a design after compiling it to a different HDL depends on the Clash compiler adding type annotations. This requires an overhaul of the Clash compiler beyond the scope of the project.

The second system, Tydi-Clash, is a library for the Tydi streaming specification in Clash. Tydi was designed around transferring complex data structures, and allows for multiple related streams carrying typed, multi-dimensional data. The Tydi-Clash library supports Tydi data types, physical streams, and logical stream constructs. To encourage correct usage of the streams, the internal signals are encapsulated in algebraic and abstract data types that prevent defining or accessing undefined values. Additionally, tests are supplied for behavioral restrictions. An example implementation revealed implementations using Tydi-Clash are unfortunately still a bit cumbersome, but this is believed to be solvable by adding a library of utility modules for common situations.

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.

The usage of robots replacing human tasks has become more prevalent. Controlling multiple of these robots can be useful in applications such as disaster response, surveillance and exploration. This form of control is often achieved by using geometric patterns, such as triangles, squares, etc. Drones can employ this concept of formation control to fly and maneuver through environments and obstacles.
In this work a distributed affine formation control algorithm is implemented onto a Crazyflie drones from Bitcraze. Ultra-wideband is used for positioning and communication between the drones. The implementation of the affine formation control algorithm is optimised such that it is only executed when new information is available, to prevent onboard microcontroller from bottlenecking. This resulted in the drones flying in formation successfully with an accuracy of approximately 6.80 cm from its expected position.
Additionally, this algorithm is extended to manage cases where unexpected missing drones could comprise the stability of the formation. The implementation uses the CMSIS library that is optimised for matrix operations. This resulted in the drones flying in formation successfully even in the case of an observation loss with an accuracy of approximately 17.69 cm.
This work not only provides empirical data of experiments with an affine formation control algorithm, but also provides a baseline implementation for future research in the field of affine formation control, which can potentially lead to noise analysis or the application affine formation controls under different circumstances.

This study aims to systematically design and implement a voltage regulation control loop (VRCL) for an unspecified power management unit (PMU) to maintain the desired load voltage for electronics worn by group-housed rodents. This is crucial for analyzing the spontaneous behaviour of group-housed rodents, focusing on social interactions and environmental exploration in a simulated natural environment.

The study explores a potential high-level design of a power link, selecting resonant inductive coupling for wireless power transfer (WPT), enabling continuous power supply over larger areas (for instance, 5 by 5 meters). A literature review reveals a research gap in integrating encircling and underneath configurations for sufficient uniform power distribution. A block diagram of the WPT system is provided, outlining the transmitter and headstage receiver components. Subsequently, a hybrid layout is suggested and significant challenges like optimizing driving current and minimizing angular and vertical misalignments are addressed.

Next, a control loop is systematically designed and implemented. Potential loads and a PMU are
selected, followed by the development and verification of an ideal power converter and its derived
and proposed plant model. Control specifications derived from this model suggest the tuning of the
controller parameters using a tailored model-based control system approach. MATLAB simulations
confirm that the control specifications are met. A non-ideal power converter is then integrated with the control loop, including ideal gate drivers, a voltage-controlled oscillator, a bandgap reference, and a PI controller. Simulation results show that the control loop meets the specifications. Despite limitations in robustness, particularly regarding load and input voltage transient response, the study also highlights the need for verification of the power converter model for load capacitors in the pF-nF range, and identifies discrepancies in overshoot behaviour. Future work includes an analysis of the system’s robustness during controller tuning and the incorporation of a transient controller. The combination of the suggested plant model and model-based tuning approach offer an alternative option for the power converter’s control loop design.

The device that is designed and described in this thesis aims to non-invasively stimulate neural activity in an earthworm at multiple, specific locations. This is done by inducing an electric field from two inductors with a specific electric field strength in mind. The response of the earthworm is recorded and amplified to enable analysis of this signal. Initially, requirements are set that need to be met by this device. From these requirements a design process was started and simulations were made. After this, a device was created on which the requirements could be evaluated. Due to the physical trials not being completed the requirements could not be fully verified.

We conduct a simulation study of an insect-inspired navigation method that combines visual learning in a small area around a home location with path integration to successfully navigate over distances 8 to 10 times larger than the learning radius, while only requiring 6MB of memory. Our simulations show that the method is capable of learning with on-board data only and handling the noise levels and errors it may encounter in real-world conditions. Furthermore, we investigate the integration of online learning with a structured buffer to mitigate catastrophic forgetting, allowing continuous learning during flight. Additional experiments assess the system's generalization capabilities beyond the learned area, its robustness to small variations in pitch and roll, and the relationship between network size and the learnable area size.

To support the exploration of the Moon, wireless sensor networks could be deployed. However, using a very large number of tiny sensing nodes to collect data in such a harsh environment has many challenges. Integrated circuits are exposed to a wide temperature range (-253 °C / +120 °C) during the lunar days and nights. To meet the very constrained power budget of an autonomous node, the node time references must consume ultra-low power while maintaining an accurate frequency. To allow accurate operation over the wide target temperature range and be robust to radiations, this thesis proposes to lock the frequency of a frequency locked loop (FLL) to an LC filter. In the FLL, a high-frequency low-power ring oscillator is locked to a separate LC filter to potentially save power with respect of a standard LC oscillator. To further save power, the FLL is heavily duty-cycled and turned on only to calibrate a lower-frequency oscillator to compensate its drift due to noise and environmental changes, thus achieving ultra-low-power dissipation. This thesis explores the design and implementation of the first LC-based FLL by proposing a system architecture, analyzing its limitations and proposing a transistor-level implementation. The resulting time reference, when implemented in TSMC 40-nm process, consumes 773.29𝜇𝑊 power and achieved an estimated 41.89𝑝𝑝𝑚/ 𝑜𝐶 temperature coefficient within an 0.389𝑚𝑚2 chip area.

This thesis project has been done with Crownstone, a subsidiary company of Almende. One of their project goals was to develop indoor localization algorithms to determine room-level location of an asset within the Crownstone network for smart-building, home automation and healthcare applications. An asset is a wireless device transmitting Bluetooth messages that are heard by Crownstones(sensors) and measure the strength of the received signal(RSSI). The terms position and location are used to denote two different concepts in this thesis. Position refers to estimating the specific coordinates, whereas a location refers to a much wider space like a room. In this thesis, we are interested in getting a location estimate.
There are two most widely used and researched localization techniques to determine the location of the asset. First, a model based(MB) method (e.g. Trilateration algorithm) which uses a mathematical model based on distance and second is a data-driven(DD) method (e.g. Fingerprinting algorithm) that relies on existing data, like RSSI to directly get the location. Algorithms are tested on real data collected by the Crownstones at the Almende office(test environment) divided into a finite number of locations or rooms. Metrics are defined based on the requirements of Almende to compare the MB algorithms with the DD algorithms. In this thesis, firstly, a centralized multilateration(MB-C) algorithm is implemented taking into account distances from N Crownstones at the office. Since one of the requirements was to perform in-network localization, a simple averaging consensus based distributed(MB-D) algorithm was selected and compared against the MB-C algorithm. Results show that the MB-D algorithm is faster, scalable and robust against single-point of failure than the MB-C but is less accurate and does not converge to the centralized solution for a noise variance greater than 10dB.
The MB algorithms have limitations in terms of selecting a model, learning the
model parameters and an additional step of mapping the position output of the implemented MB algorithms to a location is also required. To deal with these challenges, a Machine-learning(ML) based data-driven algorithm is proposed. In this, training datasets were iteratively improved with different features. Then, an Ensemble based centralized ML algorithm (DD-C) is implemented, giving a classification accuracy of 65%. Algorithm is further improved by distributed data handling leading to a classification accuracy of 77%. There has been very little to no study on finding the room-level location of an asset in an indoor setting using a distributed ML based data-driven algorithm. A consensus based distributed ML algorithm (DD-D) is proposed that performs local predictions within the Crownstone network using the same globally trained model giving a classification accuracy of 73%.
The results show that the proposed DD algorithms perform better than the MB
algorithms in terms of accuracy and are comparable in terms of prediction time. Results also indicate the proposed DD algorithms are more scalable, robust against noise but are computationally expensive.

Single-photon detection is a critical step in both quantum computing and quantum information technology. For instance, the measurement of qubit states and the establishment of entanglement in nitrogen vacancy (NV) center quantum computing requires the detection of single photons. Superconducting nanowire single-photon detectors (SNSPDs) are very competitive devices due to their performance in high detection efficiency, high count rates, low dark count rates, and low jitter. However, the readout electronics are usually implemented at room temperature by connecting the dilution refrigerator through coaxial cables, while SNSPD need to work in a cryogenic environment that is close to the qubits. In order to have better integration and reduce the complexity of the wiring when the number of qubits increases, it is essential to position the electronics close to, or even integrated with, the SNSPD and qubits.
In this thesis, a cryogenic CMOS readout for SNSPD is designed and taped-out using TSMC 40 nm technology. The SNSPD is designed for color-center quantum computing, which is anticipated to work in the wavelength range of 619-620 nm and 625-750 nm, and at a temperature of 1.8 K. The readout electronics are expected to operate at 4 K. The system is required to have a detection efficiency of more than 90 % and a dark count rate of less than 1 Hz. With the help of SPICE dynamic model, the SNSPD is reproduced in Cadence Spectre for circuit design. Active quenching is implemented in the readout architecture, allowing for an increased readout resistor, which improves the output slew rate and count rates without any latching while still keeping a high bias current for a higher detection efficiency. Under a -40 degree Celsius simulation, the readout system achieved count rates greater than 20 MHz, an average jitter of 25 ps rms, and a power consumption of 36 uW, while simultaneously expecting to significantly suppress the dark count rates and after pulses.

The problem of finding a target and forming a path to it is known and is well-explored in the field of swarm robotics. Using swarm intelligence, even swarms of simple robots are capable of solving this problem. However, often these works assume that two robots can perceive each other can physically reach each other, or that robots can detect any target within communication range. These assumptions do not hold for all swarms and environments. This thesis introduces the CSP (Concurrent Searching and Pathfinding) algorithm to make path finding possible in simple swarms without relying on these assumptions. By strategically constructing and reconstructing an ephemeral network of stationary robots and relocating the rest of the robots throughout this network, swarms performing CSP can efficiently find targets with minimal risk of fracturing the network. The effect of packet loss, and swarm size, as well as the the execution time vis-à-vis creation of new anchors are shown.

The small and lightweight Lunar Zebro rover must survive in the harsh lunar environment for several Earth days following its moon landing. The mission of the rover is to map the radiation environment on the moon. The success of the entire mission depends on the Power Electronic System (PES), which supplies power to all subsystems and charges the batteries using solar panels. The current PES of the Lunar Zebro rover does not comply with all mission-specific requirements and does not perform satisfactorily when integrated into the rover. Therefore, the need for a reliable PES that conforms to all requirements arises for the Lunar Zebro rover.

In this research, the design and implementation of an efficient, compact, and redundant PES for the Lunar Zebro rover is developed. First, the optimal Direct Current (DC) bus is designed to obtain a system with the highest efficiency. This is done by modelling the efficiency of the DC/DC converters for different bus voltages and estimating the overall losses in these converters during the deployment of the rover. Moreover, the effect of the bus voltage on the size of the passive components is investigated, and the bus voltage resulting in the most compact system is obtained. It is found that a 12 V bus results in the most efficient and compact system. No additional converter is required that regulates the 12 V output, and the inductance required for each converter is decreased compared to higher bus voltages.

Besides the DC bus design, redundancy methods are compared to obtain the best tradeoff between redundancy and footprint added. The two-phase interleaved converter was found to have only an 8.59% increase in footprint compared to single-phase converters, while failure in a switch, diode, input capacitor, and output capacitor are accounted for in each converter. Finally, the mode of operation that results in the highest efficiency is obtained by designing each converter and modelling the corresponding losses for Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) operation. For both the single-phase and two-phase interleaved converters hold that operating in CCM results in a significant increase in efficiency compared to DCM operation. Moreover, the PES utilising two-phase interleaved converters is more efficient during rover operation than the single-phase counterpart. However, charging is less efficient than for the single-phase counterpart. Simulink and LTspice simulations have been carried out to verify the operation of each converter. Finally, experiments on a functional prototype are carried out to provide experimental validation of the design.

Demand for smart, Internet-connected devices and other electronics has increased dramatically in recent years. This increase in demand for technological devices, driven by advancements in Artificial Intelligence (AI), the Internet of Things (IoT), and autonomous systems, has exposed the digital system to potential security threats. As more devices access personal data, data protection and security have become increasingly challenging. The rise in security incidents, such as the MOVEit attack and the NeBu data breach, highlights the urgency for better security systems. Malicious attacks can manifest at different levels, ranging from the network and system to the circuit level. Power side-channel attacks, which exploit variations in power consumption to obtain sensitive information, have proven effective against modern cryptographic implementations. In response to the threats posed by such attacks, researchers have diligently focused on devising countermeasures. Current research efforts focus on developing these countermeasures, yet evaluation only happens in pre-silicon scenarios in simulation or on an FPGA. Evaluating the effectiveness of those countermeasures on real Systems on Chips (SoCs) is challenging.

The thesis introduces a novel framework to bridge the gap between pre and post-silicon power side channel assessment, allowing accurate characterization and comparison of cryptographic designs before and after manufacturing. This approach aims to provide comprehensive profiling and ensure the security of future cryptographic designs. The suggested platform was verified by employing a series of different versions of the Advanced Encryption Standard (AES) algorithm that were integrated into a full System on Chip (SoC). As a metric for determining whether or not the proposed platform is suitable, attacability is being measured via correlation power analysis (CPA). Pre-silicon enables the evaluation of each AES core in isolation from the other components of the system, hence having noise-free power traces. However, in post-silicon, in order to provide a correct analysis, the platform incorporates a wide variety of features, such as automation execution, trace alignment, and noise reduction of power traces. In order to improve the automated execution process, a Left Feedback Shift Register (LFSR) has been devised to introduce a level of randomness in the encrypted messages and keys used by the cryptographic engine. In the context of trace alignment, the platform has been specifically built to include a trigger signal that serves the purpose of identifying the start/stop of the execution. Clock-gating is used as a means of mitigating noise by freezing the operation of additional cores, hence preventing the generation of extraneous auditory disturbances. In addition, the suggested approach is specifically tailored for the manufacturing process using the 40nm TSMC technology, including a simulation evaluation. Finally, to facilitate power measurements on the platform, custom printed circuit boards(PCB's) and a software backend were designed to support real life measurements and the final correlation of the pre and post-silicon power side channel behaviour. The results of our study suggest that both Pre-silicon (i.e., standalone evaluation) and Post-silicon (i.e., system evaluation) provide similar levels of accuracy in assessing attackability.

We present a computationally cheap 3D bug algorithm for drones, using stereo vision. Obstacle avoidance is important, but difficult for robots with limited resources, such as drones. Stereo vision requires less weight and power than active distance measurement sensors, but typically has a limited Field of View (FoV). In addition, the stereo camera is fixed on the drone, preventing sensor movement. For obstacle avoidance, bug algorithms require few resources. We base our proposed algorithm, Frustumbug, on the Wedgebug algorithm, since this bug algorithm copes with a limited FoV. Since Wedgebug only focuses on 2D problems, the Local-epsilon-Tangent-Graph (LETG) is used to extend the path planning to 3D. Disparity images are obtained through an optimised stereo block matching algorithm. Obstacles are expanded in disparity space to obtain the configuration space. Furthermore, Frustumbug has an improved robustness to noisy range sensor data, and includes reversing, climbing and descending manoeuvres to avoid or escape local minima. The algorithm has been extensively tested with 225 flights in two challenging simulated environments, with a success rate of 96%. Here, 3.6% did not reach the goal and 0.4% collided. Frustumbug has been implemented on a 20 gram stereo vision system, and guides drones safely around obstacles in the real world, showing its potential for small drones to reach their targets fully autonomously.

Autonomous agents are the future of many services and industries such as delivery systems, surveillance and monitoring, and search and rescue missions. An important aspect in an autonomous agent is the navigation system it uses to traverse the environment. Not much emphasis has been paid in the past on autonomous agent navigation in cluttered environments. Cluttered and unknown environments such as forests and subaquatic environments need to have autonomous navigation systems developed just for them due to their uncertain and changing nature. Path planning algorithms are used for the navigation of an autonomous agent in an environment. The agent needs to reach a target location while avoiding the obstacles it detects along the path. Such a system is called a Detect and Avoid (DAA) system and there are different implementations for it of which some are explored in this thesis. The Artificial Potential Fields method or APF for short is a method for mobile agent navigation which is based on generating an attractive force on the agent from the target and a repulsive force from the obstacles. This leads to the agent reaching the target while avoiding the obstacles along the way. The Classical APF (CAPF) method works for structured environments well but not for cluttered environments. The CAPF method can be replaced with a modified version where the agent is surrounded by a set of points (called bacteria points) around its current location and the agent moves by selecting a bacteria point as a future location. This method is named the Bacteria APF (BAPF) method. This selection happens through combinatorial optimization based on the potential value of each bacteria point. In this thesis, we propose two distinct contributions to the BAPF method. The first one being the use of an adaptive parameter in the repulsive cost function which is determined through a brute-force search. The second addition is a branching cost function that changes the value of the repulsive potential based on predefined perimeters around each obstacle. We show through simulations on densely and lightly cluttered environments that this Improved BAPF (IBAPF) method significantly improves the performance of the system in terms of the convergence to the target by almost 200% and reduced the time it takes to converge by around 25% as well as maintain the safety of the navigation route by keeping the average distance from obstacles around the same value.

Manufacturers of CubeSats prefer the use of COTS electronic components such as microcontrollers (MCU) and SDRAM but these components are vulnerable to errors caused by radiation. A specific type of error caused by radiation are soft errors which can be corrected by Error Detection and Correction (EDAC) methods. Unfortunately, it is uncommon for MCUs to have an (optimal) EDAC solution integrated in their memory controller. In these cases an external solution is required.
This work proposes a generic methodology towards transparent FPGA-based correction of soft errors that can be applied given a specific microcontroller and its off-chip main memory. To determine how powerful the EDAC solution must be the methodology uses reliability models to evaluate the MTTF of the memory system. Then, the methodology describes how the EDAC logic must be designed in the FPGA fabric such that the FPGA and its EDAC logic are transparent to the MCU. A method to periodically scrub the memory transparently to the processor is also included.
A major trade-off of the FPGA based design is that it requires the clock of the MCU memory controller to be lowered to meet the timing requirements of the SDRAM interface. For some designs it is also necessary to lower the memory capacity or to use more SDRAM devices to store the ECC parity bits.
Lastly, a hardware demonstrator was built to provide EDAC capabilities to the onboard computer developed by ISISPACE. The design consisting of an ARM9 microcontroller, an FPGA and two SDRAM devices is capable of correcting a single error in each byte of the 32- bit SDRAM interface of the MCU and can scrub the entire memory in 33 seconds. Software benchmarks showed that computing performance is about half of the original board as a result of halving the memory controller clock.

Remotely controlled vehicles have gained increased interest and application, like the exploration of inhospitable environments. To this end hexapods with C-shape locomotors are particularly suitable because these vehicles possess both the efficiency of wheels and the climbing capabilities of legged robots. Currently all C-legged hexapods are equipped with the same C-shape tip starting in the center of rotation, yet few to no alternative shapes that could improve the performance have been investigated. The performance is mainly characterized by three measures: traction, climbing and mobility. However, it was also found that no system level performance analysis including constraints exists. In this work a novel method is proposed to describe the system level performance of C-legged hexapods. Using this model, a computer-aided manual optimization of the leg shape is performed, from which it is observed that there is no unique optimal leg shape. The optima depend on the weight factor posed by the designer; but several leg types prove to perform better than the conventional shape. The most prominent trade-off of this hybrid shape is present between climbing and mobility, but this effect can be superseded when the shape is generated conceptually instead of analytically.

A miniaturized instrument was developed to enable in-situ measurements of the radiation environment at the Moon. The instrument is designated to function as the science payload for the first mission of the Lunar Zebro nano-rover. Characterization and testing of the Floating Gate Dosimeter (FGDOS) was advanced with an emphasis on its utilization as the core detector for this payload. A prototype of the Radiation Payload was designed, produced and tested. Radiation environment prediction and analysis was performed for various mission phases using SPENVIS and OLTARIS. A radiation transport model of the payload was prepared, as a foundation for more extensive simulations in the future.

This thesis highlights the need for further research and development of the FGDOS technology, including experimental mapping of the complete envelope of FGDOS sensitivity based on expected mission radiation environments. Additional noise reduction measures and thermal characterization at mission conditions are needed to iterate and improve the payload design such that it can be made flight-worthy.