Sensor | EPFL Graph Search

A sensor is a device that produces an output signal for the purpose of sensing a physical phenomenon. In the broadest definition, a sensor is a device, module, machine, or subsystem that detects events or changes in its environment and sends the information to other electronics, frequently a computer processor. Sensors are used in everyday objects such as touch-sensitive elevator buttons (tactile sensor) and lamps which dim or brighten by touching the base, and in innumerable applications of which most people are never aware. With advances in micromachinery and easy-to-use microcontroller platforms, the uses of sensors have expanded beyond the traditional fields of temperature, pressure and flow measurement, for example into MARG sensors. Analog sensors such as potentiometers and force-sensing resistors are still widely used. Their applications include manufacturing and machinery, airplanes and aerospace, cars, medicine, robotics and many other aspects of our day-to-day life. Th

Guidance, Navigation and Control for Autonomous Rendezvous and Docking of Nano-Satellites

Camille Sébastien Pirat

Is it possible to dock CubeSats in Low Earth Orbit? Challenges are associated with the level of miniaturisation: the docking accuracy is driven by the docking mechanism dimensions. The achievable docking performance with the Guidance, Navigation and Control (GNC) subsystem is constrained by the use of small sensors and actuators. A docking mechanism prototype, tailored for CubeSats was designed, built and tested in the laboratory using a simple experimental setup. Results showed that a lateral precision of 1 cm and a relative angular alignment better than 2 degrees are required to guarantee successful docking. A filtered monocular camera on the chaser satellite and various arrays of light-emitting diodes on the target vehicle were selected for the metrology, due to their potential to achieve a high relative navigation accuracy and to ensure the observability of the system throughout the final approach trajectory. Both docking mechanism and metrology system are contained within 0.5U volume and can thus be used on a wide span of satellite types. The chaser and target have a complete 3-axis attitude pointing capability and are equipped with off-the-shelf CubeSats attitude sensors and actuators, including star trackers and reaction wheels. The chaser is further equipped with a six degrees of freedom low-thrust cold gas propulsion system. The navigation and control functions rely on a linearised coupled dynamic, which includes fuel sloshing effects, and describes the 6 degrees of freedom relative motion between the chaser and target docking ports. Using this dynamic, LQR, LQI, H-Infinity and Mu-Synthesis controllers were investigated and their stability and performance assessed by mean of Mu-Analysis. A comprehensive simulation framework was developed to evaluate the performances of the navigation and control functions and the impact of actuator and sensor errors on the overall docking performance. The environmental and internal perturbations, such as atmospheric drag and fuel sloshing were taken into account. All sensors and actuators required for the docking were modelled, including realistic errors and noise characteristics. Different docking scenarios were investigated based on the different behaviour of the coupled dynamics. Worst-case scenarios such as the loss of sensor signals prior to docking are also tackled. The robustness of the proposed control schemes was assessed by the use of structured singular values. Complementary non-linear Monte-Carlo simulations were also performed to determine the complete GNC performances as well as fuel consumption. Results show that the proposed GNC is robust to the modelled sensor and actuator noises, fuel sloshing, dynamics uncertainties and that a lateral position accuracy better than 5 mm can always be obtained at docking. Furthermore, docking is not affected by the loss of star tracker signals nor by harsh illumination conditions, and can thus take place on a variety of orbits.

EPFL2018

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature values in different geographical locations, information diffusion in social networks, functional activities in the brain, or 3D meshes in computer graphics. The representation, analysis, and compression of such data is a challenging task and requires the development of new tools that can identify and properly exploit the data structure. In this thesis, we formulate the processing and analysis of structured data using the emerging framework of graph signal processing. Graphs are generic data representation forms, suitable for modeling the geometric structure of signals that live on topologically complicated domains. The vertices of the graph represent the discrete data domain, and the edge weights capture the pairwise relationships between the vertices. A graph signal is then defined as a function that assigns a real value to each vertex. Graph signal processing is a useful framework for handling efficiently such data as it takes into consideration both the signal and the graph structure. In this work, we develop new methods and study several important problems related to the representation and structure-aware processing of graph signals in both centralized and distributed settings. We focus in particular in the theory of sparse graph signal representation and its applications and we bring some insights towards better understanding the interplay between graphs and signals on graphs. First, we study a novel yet natural application of the graph signal processing framework for the representation of 3D point cloud sequences. We exploit graph-based transform signal representations for addressing the challenging problem of compression of data that is characterized by dynamic 3D positions and color attributes. Next, we depart from graph-based transform signal representations to design new overcomplete representations, or dictionaries, which are adapted to specific classes of graph signals. In particular, we address the problem of sparse representation of graph signals residing on weighted graphs by learning graph structured dictionaries that incorporate the intrinsic geometric structure of the irregular data domain and are adapted to the characteristics of the signals. Then, we move to the efficient processing of graph signals in distributed scenarios, such as sensor or camera networks, which brings important constraints in terms of communication and computation in realistic settings. In particular, we study the effect of quantization in the distributed processing of graph signals that are represented by graph spectral dictionaries and we show that the impact of the quantization depends on the graph geometry and on the structure of the spectral dictionaries. Finally, we focus on a widely used graph process, the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that depends on the graph topology and exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm.

EPFL2016