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1. Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility

   https://doi.org/10.1109/ITSC45102.2020.9294425  

   Rodionova, Alena ; Pant, Yash Vardhan ; Jang, Kuk ; Abbas, Houssam ; Mangharam, Rahul ( January 2020 , IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC))

   null (Ed.)

   With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities. Unlike traditional human-inthe-loop air traffic management, UAM requires decentralized autonomous approaches that scale for an order of magnitude higher aircraft densities and are applicable to urban settings. We present Learning-to-Fly (L2F), a decentralized on-demand airborne collision avoidance framework for multiple UAS that allows them to independently plan and safely execute missions with spatial, temporal and reactive objectives expressed using Signal Temporal Logic. We formulate the problem of predictively avoiding collisions between two UAS without violating mission objectives as a Mixed Integer Linear Program (MILP). This however is intractable to solve online. Instead, we develop L2F, a two-stage collision avoidance method that consists of: 1) a learning-based decision-making scheme and 2) a distributed, linear programming-based UAS control algorithm. Through extensive simulations, we show the real-time applicability of our method which is ≈6000× faster than the MILP approach and can resolve 100% of collisions when there is ample room to maneuver, and shows graceful degradation in performance otherwise. We also compare L2F to two other methods and demonstrate an implementation on quad-rotor robots. 

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2. A sensorless drone-based system for mapping indoor 3D airflow gradients: demo abstract

   https://doi.org/10.1145/3498361.3538671  

   Liu, Yanchen ; Zhao, Minghui ; Xia, Stephen ; Wu, Eugene ; Jiang, Xiaofan ( June 2022 , Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services (MobiSys '22))

   With the global spread of the COVID-19 pandemic, ventilation indoors is becoming increasingly important in preventing the spread of airborne viruses. However, while sensors exist to measure wind speed and airflow gradients, they must be manually held by a human or an autonomous vehicle, robot, or drone that moves around the space to build an airflow map of the environment. In this demonstration, we present DAE, a novel drone-based system that can automatically navigate and estimate air flow in a space without the need of additional sensors attached onto the drone. DAE directly utilizes the flight controller data that all drones use to self-stabilize in the air to estimate airflow. DAE estimates airflow gradients in a room based on how the flight controller adjusts the motors on the drone to compensate external perturbations and air currents, without the need for attaching additional wind or airflow sensors. 

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3. Detection of weakly bound clusters in incipiently sooting flames via ion seeded dilution and collision charging for (APi-TOF) mass spectrometry analysis

   https://doi.org/https://doi.org/10.1016/j.fuel.2020.119820  

   Carbone, Francesco ; Canagaratna, Manjula ; Lambe, Andrew ; Jayne, John ; Worsnop, Douglas ; Gomez, Alessandro ( January 2021 , Fuel)

   Suuberg, Eric (Ed.)

   This study introduces an atmospheric pressure chemical ionization method that relies on low-energy thermal collisions (i.e., <0.05 eV) of aerosolized analytes with bipolar ions pre-seeded in a sample dilution flow and allows for the detection of weakly bound molecular clusters. Herein, the potential of the method is explored in the context of soot inception by performing mass spectrometric analysis of a laminar premixed flame of ethylene and air whose products are sampled through a tiny orifice and quickly diluted in nitrogen pre-flowed through a Kr85 based neutralizer to generate the bipolar ions. Analyses were performed with an Atmospheric Pressure Interface Time-of-Flight (APi-TOF, Tofwerk AG) Mass Spectrometer whose high sensitivity, mass accuracy, and resolution (over 4000) allowed for the discrimination of the flame products from the pre-seeded ions. Since ionization of neutrals occurs by either ion attachment or charge exchange following ion collision, the identification of the origin of each peak in the measured mass spectra is not-trivial. Nevertheless, the results provide valuable information on the overall elemental composition of the neutral flame products ionized in either polarity. Results show that the clustering of hydrocarbons lighter than 400 Da and having a C/H ratio between 2 and 3 leads to soot inception in the flame. The dehydrogenation of the flame products, expected to occur as they are convected in the flame, is observed only for measurements in positive polarity because of a higher probability of soot nuclei and precursors to get a positive rather than a negative charge. 

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4. Large field-of-view thermal imaging via all-silicon meta-optics

   https://doi.org/10.1364/AO.493555  

   Wirth-Singh, Anna ; Fröch, Johannes E. ; Han, Zheyi ; Huang, Luocheng ; Mukherjee, Saswata ; Zhou, Zhihao ; Coppens, Zachary ; Böhringer, Karl F. ; Majumdar, Arka ( January 2023 , Applied Optics)

   A broad range of imaging and sensing technologies in the infrared require large field-of-view (FoV) operation. To achieve this, traditional refractive systems often employ multiple elements to compensate for aberrations, which leads to excess size, weight, and cost. For many applications, including night vision eye-wear, air-borne surveillance, and autonomous navigation for unmanned aerial vehicles, size and weight are highly constrained. Sub-wavelength diffractive optics, also known as meta-optics, can dramatically reduce the size, weight, and cost of these imaging systems, as meta-optics are significantly thinner and lighter than traditional refractive lenses. Here, we demonstrate 80° FoV thermal imaging in the long-wavelength infrared regime (8–12 µm) using an all-silicon meta-optic with an entrance aperture and lens focal length of 1 cm. 

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5. Autonomous Flying With Neuromorphic Sensing

   https://doi.org/10.3389/fnins.2021.672161  

   Parlevliet, Patricia P. ; Kanaev, Andrey ; Hung, Chou P. ; Schweiger, Andreas ; Gregory, Frederick D. ; Benosman, Ryad ; de Croon, Guido C. ; Gutfreund, Yoram ; Lo, Chung-Chuan ; Moss, Cynthia F. ( May 2021 , Frontiers in Neuroscience)

   null (Ed.)

   Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control. 

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