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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. Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

   https://doi.org/10.3390/app12020576  

   Kim, Joseph; Atkins, Ella (January 2022, Applied Sciences)

   Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM. 

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3. Collision avoidance strategies for advanced air mobility using UAS-to-UAS communications

   https://doi.org/10.1139/dsa-2024-0044  

   Mandapaka, Jaya Sravani; McCorkendale, Logan; McCorkendale, Zachary; Kidane, Mathias Feriew; Smith, Nathan; Hawkins, Skyler; Namuduri, Kamesh (January 2025, Drone Systems and Applications)

   Advanced air mobility (AAM) has introduced a new mode of air transportation that can be integrated, providing services including air taxis, which can quickly transport people and cargo from one place to another. However, urban airspace is already congested with commercial air traffic, so there is a need for an efficient and autonomous airspace management system. Establishing structured air corridors and enabling UAS-to-UAS (U2U) communications are essential to achieve autonomy. Air corridors are designated airspace primarily reserved for AAM traffic, which will streamline the movement of unmanned aircraft systems (UAS). Meanwhile, U2U communications facilitate efficient collision avoidance strategies (CAS). A key aspect of this system is the development of CAS, which requires advanced communication protocols to monitor traffic patterns and detect potential collisions. This paper explores designing and implementing CAS using U2U communications. Use cases for U2U communications include merging, minimum separation, information relay, collaborative sensing, and rerouting. All these use cases demand real-time solutions for managing traffic conflicts involving multiple UAS. The CAS discussed in this paper utilizes U2U communications to mitigate the risk of collisions in the airspace and demonstrates how U2U communications can assist in efficient AAM traffic management through simulations. 

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4. Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

   https://doi.org/10.3390/drones8100590  

   McCorkendale, Zachary; McCorkendale, Logan; Kidane, Mathias Feriew; Namuduri, Kamesh (October 2024, Drones)

   With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces. 

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5. The Philosophy of UAS-to-UAS Communications

   Namuduri, K; Mandapati, Jaya S; Kidane, Feriew M (May 2024, International Academy, Research and Industry Association (IARIA))

   With an increase in the use of Unmanned Aircraft Systems (UAS) and the inevitable integration of UAS in every imaginable industry, there is a need for enhanced situational awareness and information sharing. Traditional approaches are not sufficient for UAS as they are typically designed for human involvement in the decision-making process. Novel solutions based on variety of sensors are being developed for object detection and avoidance. This paper presents UAS-to-UAS (U2U) communication as a means to enhancing situational awareness and safety in the airspace. U2U communication is essential for enabling UAS to operate cooperatively, avoid collisions, and respond to dynamic scenarios in the airspace. As airspace is a shared resource regulated by federal agencies, such as the Federal Aviation Administration (FAA) in the United States, certain questions, such as which aircraft has the right of way, need to be addressed unambiguously. This paper focuses on the philosophy of U2U communications using the five use-case scenarios proposed by standard organizations. The outcome of this research serves as a potential input and guidance for regulatory agencies. 

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