Ultrasonic Phased Arrays for 3D Sonar Imaging in Air

Next-gen autonomous mobile robots are not only required to navigate in a wide variety of challenging environments, but also have to interact directly with humans. In order to ensure reliability and safety, the integration of different and complementary perception sensor technologies is crucial. In particular, ultrasonic sensors stand out due to their robust operation in difficult lighting conditions, in the presence of transparent and reflective objects, and in smoke-filled and dusty environments, so that they ideally complement lidar and camera systems. However, conventional one-dimensional ultrasonic range finders restrict the available navigation capabilities of highly maneuverable robots. Therefore, in this thesis, three-dimensional sonar perception sensors based on air-coupled ultrasonic phased arrays and beamforming are investigated, which are capable of simultaneously localizing multiple objects in terms of distance, direction and height, enabling to form an image of the environment. The focus of this work is the conception and realization, as well as the numerical and experimental evaluation of five sonar imaging systems, which pursue different optimization goals in order to highlight the real-world capabilities and limitations. Two of the sonar prototypes created consist of 64 piezoelectric ultrasonic transducers (PUTs) with a narrowband resonant frequency of 40 kHz, all of which are utilized for both, transmit and receive beamforming. The single-line-acquisition technique and the resulting array gain enable imaging within a long range of over 6 m. The corresponding transceiver electronics, FPGA and system architecture, as well as the implementation details of the GPU-accelerated frequency-domain array signal processing and visualization using Nvidia CUDA and OpenGL are described. One of the systems utilizes a waveguide structure in which the PUTs are inserted to form a uniform dense λ/2 array geometry, that allows grating-lobe-free beamforming. The other system prototype exploits a non-uniform sparse spiral array configuration to span a large aperture for achieving a high angular resolution of 2.3°, enabling to recognize patterns and shapes of objects, e.g. a hand. Two further minimalistic embedded sonar systems are particularly designed for hardware-limited and mobile applications. These concepts use narrow-band PUTs for transmitting and wide-band digital MEMS microphone arrays for receiving. Each system is based on the multi-line acquisition technique, requiring only a single pulse for image formation, and, thus, providing high frame rates of 30 Hz. One of the systems consists of one PUT and a hexagonal 36-element microphone array, whereas the signal and 3D image processing is handled by an FPGA and a GPU-accelerated single-board computer (Nvidia Jetson Nano). The other system requires only a single microcontroller and relies on a waveguided PUT line array paired with a microphone line array in a T-configuration. Moreover, array design strategies are introduced that combine two non-uniform spiral sub-arrays featuring different element densities, which achieve a lower side lobe level for the same main lobe width compared to existing density tapering modifications. Based on this geometry, a sonar system with 64 MEMS microphones is developed, which additionally uses three waveguided PUTs to sequentially transmit different frequencies, whose resulting individual images are merged into a compound image. This sonar system thus achieves an advantageous trade-off between angular resolution and image contrast while maintaining a high frame rate and range. All of these system prototypes are analyzed with respect to their transmit, receive, and pulse-echo characteristics, as well as their achievable imaging quality in an anechoic chamber. In addition, the relative amplitude and phase errors of the different transducer technologies are investigated, the effects of these errors on the beamforming are analyzed using Monte Carlo simulations, and the improvements after the experimental calibration are highlighted. Furthermore, image enhancement by post processing is investigated using an autoencoder neural network, trained to suppress the typical transmit pulse, main lobe, and sidelobe characteristics. All in all, this work highlights the broad application potential of 3D sonar systems, as they provide valuable localization information, that surpasses conventional 1D range sensors, contributing to the advancement of emerging technologies in autonomous vehicles, robotics, and industrial environments