Global Navigation Satellite Systems (GNSS) like GPS have become part of everyday life. From navigation as was its original intent to highly accurate and relatively cheap timekeeping and making games out of walking outside.
However, tools to test GNSS-enabled systems can be expensive, especially for non-GPS systems or they bypass the actual GNSS receiver.
Therefore I made a free and open-source GNSS simulator framework and implemented five different GNSS signals on top of it (GPS L1 C/A, GLONASS L1, Galileo E1, Beidou B1I, and IRNSS L5). This thesis document will explain how the framework works and evaluate the five simulators.
Because easy access to GNSS simulators means more people can abuse it I will also cover spoofing and spoofing detection.
While spoofing can be used for nefarious purposes I show how it can potentially be used to protect from GNSS guided drones.

Neuromorphic architectures are energy efficient architectures for executing spiking neural networks. Current open-source neuromorphic hardware projects are either experimentation platforms (RANC, ODIN) or neural network accelerators (Open-Spike, SNE), there are no direct processing platforms that support AI and ML applications. Seneca-Lite is an open-source RISC-V based multicore neuromorphic platform. The goal of Seneca-Lite is to enable new possibilities for AI and ML applications and foster more collaboration in this field. The platform is intended for research and academic purposes and furthering the field of neuromorphic computing.

The Seneca-Lite platform utilizes the Ibex core, a highly parameterizable open-source 32 bit RISC-V processor. Each core in the Seneca-Lite platform contains a Network On Chip (NoC) router, local memories, network message FIFOs and interconnects. The multi-core platform is designed to facilitate messaging between cores via the NoC. The NoC used in Seneca-Lite is the same as the NoC used in RANC (Reconfigurable Architecture for Neuromorphic Computing). The number of cores in the system is parameterized and can be controlled by the user depending on their need. Since the platform is open-source, many of the internal parameters (Ibex parameters, FIFO parameters etc) can be tweaked by the user as per their target application.

The completed neuromorphic platform is benchmarked for various state-of-the-art applications and compared to other neuromorphic platforms.

This thesis explores the challenge of achieving haptic bilateral teleoperation across long distances, focusing on enhancing Model Mediated Teleoperation (MMT) to support dynamic environments under high network latency. Instead of striving for perfect model alignment, we acknowledge the inevitable mismatch between the remote environment and its model at the operator. We suggest a set of design considerations and a complementary framework to make operator intent a key factor in MMT solution development. By developing a system where a user can control a remote robot arm to draw on a moving whiteboard using a novel position-altering scheme that attempts to prioritize operator intent, we apply the framework to minimize the impact of network latency on user experience. Our findings reveal that the implemented position-altering scheme significantly reduces the effects of network delays on drawing accuracy, and a user study confirms that force feedback delays are more critical to user satisfaction than visual feedback delays. The results suggest that satisfactory teleopera-tion performance can be achieved with network latency up to 50 ms, challenging the conventional 10 ms requirement. This research provides design considerations, a comprehensive framework with a position-altering policy, and an open-source software package for advancing haptic teleop-eration technologies.

The pandemic showed the power of video calls, as it was used to accomplish a multitude of tasks that normally required physical attendance. Expanding on the video call’s transmission of sight and sound, haptic bilateral teleoperation aims to add the sense of touch and the ability to manipulate a remote envir- onment as if the user is physically present. Haptic bilateral teleoperation is a technique where a robot mimics the actions of a human operator, who in turn receives feedback from the robot’s sensors. The technique requires low-latency haptic feedback, in the range of single-digit milliseconds, which is completely im- practical when transmitting force measurements over a large distance network. Instead, the low latency requirements can be fulfilled with Model Mediated Teleoperation (MMT) to predict the user’s perceived force feedback. With the addition of force feedback, the latency of the video feedback becomes an area of greater concern. Force and video feedback in conjunction are necessary for true immersion, but using MMT to generate real-time video feedback is im- practical. This work explores a configuration where MMT is used to generate haptic feedback and video feedback is transmitted from the remote environment to the user. Transmittance of video feedback is made possible due to its relaxed latency requirements compared to force feedback. The sources of video latency can be divided into two, latency caused by the network transmission and latency from the non-network components. While minimization of both is valuable, any time saved on the non-network components can be spent on expanding the net- work and maximizing the technology’s range. In this work, we aim to answer the question ”How can we minimize the latency caused by the non-network components of a live video feedback system?” Our approach is to design a field- programmable gate array (FPGA) configuration to minimize all latencies in a live video system excluding the camera, network, and monitor components. We demonstrate a no-network live video system with 34 ms latency, of which around 5 ms originate from the FPGA. This is a marked improvement of the 63 ms latency our department achieved using a non-FPGA solution. Utilization of cameras and monitors designed for low latencies is recommended for bet- ter results, with a hypothesized latency of 11.5 ms for a high-end non-network approach.

Haptic bilateral teleoperation technology aims to transmit the sensation of touch over the internet, enabling remote work for all. However, it faces significant challenges, including stringent latency requirements (sub 1 ms) for the Tactile Internet and the need for advanced models in Model Mediated Teleoperation. This work explored the feasibility of haptic bilateral teleoperation over long distances through a new framework that leverages live video and predictive haptic feedback. This framework was implemented on a real experimental setup where users draw with a haptic device controlling a remote robotic arm on a moving whiteboard. A user study was conducted on this setup which confirmed that the method is sound and revealed that force feedback latency requirements are significantly more stringent than those of visual feedback. In this application users did not perceive the application as more difficult at 50 ms network latency (110 ms visual latency) with user experience only degrading at 75 ms added network latency. These findings prove that through the Demonstrate Imitate Adjust framework, long-distance haptic bilateral teleoperation is achievable.

The rapid advancement of automated vehicles (AVs) can potentially improve transportation. However, ensuring the safety and reliability of Automated Driving Systems (ADS) remains a critical challenge, particularly when facing the expansion of Operational Design Domains (ODDs) and the continuous emergence of unknown hazardous scenarios. This thesis aims to address these challenges by developing a framework for monitoring the safety of multi-channel ADS and identifying hazardous scenarios using Safety Performance Indicators (SPIs) and Hazardous Scenario Identification (HSI) techniques.

The proposed SPI framework, based on the principles outlined in the UL 4600 standard, encompasses a comprehensive set of metrics for assessing the safety and performance of ADS. These metrics cover various critical functionalities, such as ego localization, object detection, trajectory planning, and overall ADS behaviour. By defining appropriate thresholds for each SPI, the framework enables the identification of potential safety issues and supports the continuous monitoring and improvement of ADS.

The HSI module, developed as part of this thesis, leverages the SPI framework and the NXP Daruma cross-channel analysis to detect hazardous scenarios. The HSI module's performance is evaluated using the CARLA simulator and advanced ADS software stacks (LAV and TFUSE) across diverse driving scenarios. The results demonstrate the HSI module's effectiveness in identifying hazardous scenarios such as ego vehicle tailgating, inconsistent ego localization, and ego vehicle being tailgated. However, our analysis also reveals challenges in terms of false positives and negatives, highlighting the need for further improvements in the ADS's perception and localization functionalities and in tuning the SPI thresholds appropriately based on testing as well as the characteristics of the ADS.

This thesis contributes to advancing ADS safety by developing a comprehensive SPI framework and implementing a proof of concept HSI module. We propose an architecture that integrates these components in a closed-loop process involving vehicle fleet data collection, cloud-based analysis, and targeted software updates. This framework enables the identification of areas for improvement and supports generating OpenSCENARIO files for reproducing and analyzing hazardous scenarios ad hoc. The findings from the experimental evaluation provide valuable insights into the performance and limitations of the SPI safety monitoring and HSI techniques, guiding the safe deployment and continuous improvement of ADS. This research ultimately paves the way for the widespread adoption of automated vehicles (AVs) in driving environments.

Natural disasters often present significant challenges for rescue operations due to the complex and hazardous environments they create. Teleoperation technology, particularly haptic bilateral teleoperation, offers promising solutions to enhance the efficiency, safety, and effectiveness of disaster response. This study explores the feasibility of approximating dynamic object movement to achieve satisfactory force feedback for haptic bilateral teleoperation systems, focusing on minimizing the impact of network delays on user experience. We conducted a series of experiments and user studies to assess the system’s performance under various delay conditions. Our findings indicate that dynamic object movement can be successfully approximated to provide realistic force feedback. The user study revealed that network delays greater than 75 ms significantly impact task difficulty, and delays beyond 125 ms affect system usability, although the system remains functional up to a total delay of 200 ms. The acceptable total delay, including system latency of approximately 135 ms, is found to be around 185 ms for optimal user experience. This research contributes to the development of teleoperation systems by providing insights into the
acceptable levels of network delay and the practical implementation of force feedback mechanisms, paving the way for future advancements in the field.

Haptic bilateral teleoperation aims to create systems, where human precision and dexterity can be applied over long distances with the aid of robotic devices. These systems could make remote surgery or spacecraft repair a reality. The main challenge for such systems is latency. TU Delft's Networked Systems group is developing a teleoperation system, which combines predicted force feedback with delayed vision feedback. This project investigates the force feedback prediction of cutting interactions by implementing a simple physics based model to estimate the forces of cutting objects with a knife in a haptic simulation. The quality of the interaction and impact of visual delay is evaluated in a user study. It is found that a simple physics based cutting model can create convincing cutting interactions, which users find to be sufficiently controllable up to a delay of 75ms.

In this paper, we investigate the use of heightmap-based models to predict deformations in granular materials during teleoperation tasks. Our primary objective is to provide real-time cutting feedback and contact predictions for haptic bilateral teleoperation applications, thereby enhancing operator control in remote manipulation scenarios. We developed a simulation model that utilizes heightmaps to represent deformable materials and implemented a cutting algorithm to simulate complex interactions with granular soils.

Through a series of experiments, we demonstrated that higher resolution heightmaps significantly improve the accuracy and stability of cutting feedback. Our findings suggest that this approach is a viable method for keeping track of changes in a deformable object's shape and can be further expanded to include a wider range of teleoperation tasks, potentially improving the safety and effectiveness of remote operations in hazardous environments.

Being able to do something while not being in the same room could be benefit more people than you know. For this, the improvements made in the world of robotics and network systems world lead to using haptic bilateral teleoperation to being a vi- able option to help with this. However when con- trolling a robot, delays make the experience a lot more difficult and with very precise tasks, like sur- geon trying to make incisions, this could be unac- ceptable when going wrong as there is possibly no way back. A way to mitigate this is by using predic- tive force feedback to aid in this. For this paper, we look into if we can approximate hard physical tran- sitions, like puncturing, to gain satisfactory force feedback for haptic bilateral teleoperation. The basis to getting physical state transition was to model a needle puncturing a piece of paper. For this to be modelled, we have opted to go for a thresh- old mechanism that separates the states from not puncturing to puncturing. To get a first guess on what this threshold should be, physical testing was performed with a real needle and paper. For this actions of applying force with a needle on paper, how been recorded and from it a threshold force has been measured. This results is used for the thresh- old used in the models creation in Bullet physics engine provided by the Networked Systems group. The threshold mechanism, a friction mechanism and mechanism to apply delay on the model simu- lating the delay of the robot have been implemented for this research. This model was then tested for its behaviour over time, which resulted in it performing the way it was expected with respects to what was implemented. Then the model was evaluated in an user study on which different delays have been applied on the model, to gain insight in how immersive the expe- rience is with these delays applied which simulates the reality more when it could be used in combina- tion with a robot arm. The evaluation resulted in it being immersive, while the difficulty of the task of puncturing a piece of paper does increase the more the delay is. With hard physical transitions being able to be approximated with a the model behaving like expected and the force feedback resulting for it being satisfying enough for the user for the model to be immersive enough, makes this hard physical transitions worth continuing researching about in the future.

This paper presents a novel approach to simulating liquid interactions for haptic bilateral teleoperation without complex fluid dynamics simulations. We propose a model combining a simplified drag equation with a "position tail" mechanism to approximate force feedback for viscous and turbulent liquids. The model was evaluated through theoretical analysis, manual testing, and a user study. Results of the latter demonstrate the ability to convincingly simulate different liquid types, with participants correctly identifying viscous and turbulent liquid types. The study also revealed that visual feedback delays up to 125 ms did not significantly impact user experience. While promising, the system remains sensitive to noisy input data. This research contributes to the development of efficient and believable haptic simulations for teleoperation, paving the way for future developments.

Haptic bilateral teleoperation is a concept where operators can feel, interact with and manipulate objects in a remote environment. The latency, often introduced by a network over a long distance, on force feedback poses a problem when trying to interact with objects. Model mediated teleoperation tries to mitigate this latency, but introduces new problems. Extending on model mediated teleoperation, this thesis uses a physics simulation based model to create an end-to-end system for haptic bilateral teleoperation, implements robotic control for a Novint Falcon and a model to predict force feedback when cutting through objects. Experiments on the implemented cutting model affirm the models accuracy.

Bilateral teleoperation with force feedback aims to transmit human expertise over long distances by transferring the sensation of physical contact. One of the primary challenges in achieving this goal is the ultra low latency requirement. Tactile internet and model-mediated teleoperation are promising research areas addressing the latency constraint. In model-mediated teleoperation, it is crucial that the remote environment parameters are tracked with minimal latency to update the local model. This work investigates the potential of designing a cost-effective object tracking solution that performs comparably to state-of-the-art systems while maintaining low tracking latency. A method is proposed to streamline the design process by leveraging assumptions about the tracking conditions. An object-tracking algorithm that can effectively fuse high-frequency noisy inertial data (>1 kHz) with delayed, low-frequency (30 Hz) but accurate camera data has been designed. A practical system was constructed using cost-efficient components, and a dataset was collected for testing and characterization. The system demonstrated the ability to produce object pose estimates at 1 ms intervals. In experiments along a single axis, the system achieved a mean positional estimation error of 0.1 cm and a mean orientation estimation error of 0.5◦. The algorithm also successfully corrected for camera latencies of up to 350 ms while maintaining accurate estimates. These results demonstrate the possibility of achieving a low latency, accurate object tracking system while keeping component and computation costs reasonable.

In response to the growing demand for sustainable transportation solutions, bike-sharing systems have gained prominence in supporting an eco-friendly means of commuting. Within this landscape, Skopei, a forward-thinking company specializing in innovative sharing propositions, has developed a smart bike lock capable of autonomous rentals and returns. This master’s thesis delves into the application of radio frequency-based distance measurements to create an indoor positioning system for the efficient management of bike storage. Specifically, the system is designed to determine whether bikes have been correctly parked at docking locations, enabling users to conclude their rentals autonomously. The architecture of this system uses a network of anchor nodes (known-location routers) that should ascertain the positions of mobile nodes (bikes) within the bicycle storage area. Notably, the solution developed in this thesis employs sophisticated distance measurement techniques, including frequency hopping and phase shift analysis. By finding the amplitude and phase shift over multiple frequencies, we can find the channel impulse response and estimate the distance using machine learning. We employ a novel Multi-layer Perceptron neural network regressor to improve the accuracy in the presence of complex environmental factors in bike storage environments. In the bike storage test case, we achieved a mean absolute error in position estimation of 1.68m compared to 3.80m of a naive approach. We improved the parking state classification from 75.99% of a naive approach to 98.09% with our machine-learning-based approach. This thesis underscores the importance of cutting-edge distance measurement methods and real world field studies in advancing indoor positioning systems, specifically for smart bike storage management. By bridging the gap between technology and sustainable transportation, this work aims to make urban bike-sharing systems more scalable, efficient, user-friendly, and environmentally conscious.

This thesis explores the integration of (Frequency Shift) Chirp Spread Spectrum Modulation (FSCSSM) in Body Coupled Communication (BCC) using the Galvanic Coupling (GC) method. The goal is to provide a method of private communication in a noisy channel for devices in and around the human body. Various parameters, including frequency, electrode dimensions, electrode inter-distances, and noise levels, are evaluated. Results demonstrate that FSCSSM allows reliable communication in highly noisy channels, achieving a data rate of 9.6 kbps at a Signal-to-Noise Ratio (SNR) of -9dB. The impact of body composition is highlighted, showing its significance in single-body communication. The communication in single-body is extended by evaluating body-to-body communication, enabled by touch, as well. This research provides valuable insights for designing robust BCC-FSCSSM systems.

Platooning involves organizing a group of vehicles with common attributes into a formation. With the help of Road Side Units, they establish Intelligent Transportation System (ITS). To facilitate communication in this system, one of the prominent communication technologies is Dedicated Short Range Communication (DSRC). In Europe, the European Telecommunication Standards Institute (ETSI) standardizes DSRC as ETSI ITS-G5 in which Decentralized Congestion Control (DCC) is used to prevent and control congestion within a wireless channel. In this work, we determine a list of issues, namely traffic inefficiency and impaired communication, with the conventional formation of a platoon, especially an elongated one. Then, a new paradigm of multi-lane platoons is proposed with a list of modifications based on ETSI DCC to establish a prioritization scheme. These modifications are tailored to the characteristics and regulations of a multi-lane platoon. We also point out the issues of the current adaptive DCC parameters, which motivates us to develop a cross-layer mechanism called dynamic β. This proposal aims to improve the performance of the congestion control mechanism. Our findings demonstrate the possibility of preventing channel congestion by reducing the channel load by 22% at the highest message intensity without compromising the performance of each vehicle. For dynamic β, we manifest a higher channel utilization when employing our proposal, compared to what is achieved with the default ETSI parameters. Message delivery ratio, node capacity, periodicity, and message age are also greatly improved. In short, our work provides safer for more efficient traffic.

Tactile Internet (TI) is a pioneering network paradigm that aims to communicate haptic feedback with ultra-low latency. It will enable new ways for us to interact with remote environments, such as transferring skills over the network and controlling remote objects. One crucial component of this framework is the ability to track the position and movement of remote objects. While there are many tracking algorithms, evaluating them in TI applications can be time-consuming, expensive, and sometimes impractical. Hence, we present a virtual platform for developing, evaluating, and comparing tracking solutions. We demonstrate how our platform can be used to tune tracking algorithms and determine trade-offs between different types of hardware. In turn, this provides insights that were previously difficult to obtain using real physical platforms, as real world coordinates may not align with virtual coordinates and experiments are not easily repeatable.

The Tactile Internet (TI) aims to expand seamless interaction over the Internet by providing a new form of interaction through touch by providing haptic feedback. To realize this, the TI is limited by a round-trip latency of 1-10 ms, meaning that the TI is limited by a physical distance of 1500 km. A workaround to this requirement is the introduction of local simulations. To keep track of moving objects in these simulations, a stable tracking algorithm is needed. This algorithm is provided in the form of a particle filter. The TI requires high tracking accuracy from this algorithm, but to achieve that the algorithm becomes computationally expensive. If the movement of the to-be-tracked object is known a priori, however, the particle filter can be adapted to focus only on that movement, neglecting the other directions. This increases tracking accuracy with an equal amount of samples, thus requiring a lower amount of samples to achieve the same accuracy, reducing computational power. This paper explores how to achieve this adaptation and analyses the increase in accuracy. By adapting the filter, the tracking accuracy is significantly increased, even with a lower number of samples. This results in gaining a speedup with a factor of about $36$, while having similar tracking accuracy.

Creating a local model of a remote environment is a way to reduce latency in tactile internet. This local model contains properties that are estimated by a nonintrusive estimation method. To prevent the model from deviating increasingly from reality, the estimates should be updated once an interaction begins. This research paper investigates how the physical properties of a remote object can be estimated. This is done by exerting forces and observing the resulting motion. The physical properties that are updated include the mass and center of mass. These values are calculated to converge toward the actual value. The mass can be estimated by observing the linear motion, for which the game engine Unity is used. For the center of mass estimation, it is advised to take a different approach, since the values received by Unity were not enough to base an estimate on. This paper also investigates the influence of update frequency on estimation accuracy and the inaccuracies that the Unity physics engine presents.