Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. Dur...

https://arc.aiaa.org/doi/pdf/10.2514/1.D0091

Well-Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk

The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations in the National Airspace System. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft are based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision (NMAC). Since midair collisions are difficult to observe in simulation and are inherently rare events, basing evaluations on NMAC enables a more robust statistical analysis. However, an NMAC and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller UASonly encounters. The primary contribution of this paper is to explore quantitative criteria to use when simulating two or more smaller UASs in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assumes a historically motivated upper bound for the collision likelihood and subsequently identify the smallest possible NMAC analogs. We also demonstrate the NMAC analogs can be used to support modeling and simulation activities.

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A Quantitatively Derived NMAC Analog for Smaller Unmanned Aircraft Systems Based on Unmitigated Collision Risk

There is an increasing demand for the FAA to allow small UAS (sUAS) operations in the US national airspace system. The FAA must understand and quantify the risk of collisions with manned aircraft during desired sUAS operations in order to produce regulations and standards. The FAA has made extensive use of Monte Carlo collision risk analysis simulations in developing standards for manned aircraft collision avoidance systems, as well as separation standards and Detect and Avoid systems for large UAS. Similar Monte Carlo simulations are desired for use in collision risk simulations for sUAS operations and system development. A key component of Monte Carlo simulations are models of aircraft flight. Models for manned aircraft are developed from recorded radar data. These manned aircraft models are modified to model large UAS which fly in similar ways. However, new methods are needed to model sUAS flights since there is no corresponding dataset of flights, and their flight capabilities are significantly different from manned aircraft. We demonstrate a methodology for developing sUAS flight models that leverages OpenStreetMap, an open source geospatial information and map dataset, to generate representative unmanned operations at low altitudes for use in Monte Carlo collision risk simulations. For example we model rail line inspection flights by following rail lines, and hobbyist flights by locating parks and other recreational sites. We automatically identify geographic locations of interest for various use cases, and generate appropriate associated transit, search, and hover trajectories for different sUAS performance dynamics and cruise altitudes. We leverage the map data to identify potential variations in operational density.

Generating Representative Small UAS Trajectories using Open Source Data

With the integration of small unmanned aircraft systems into the U.S. National Airspace System, low altitude regions are being stressed in historically new ways. The FAA must understand and quantify the risk of collision between unmanned and manned aircraft during desired low altitude unmanned operations in order to produce regulations and standards. It is increasingly important to characterize how manned aircraft operate at these low altitudes, as historical assumptions are insufficient for modeling and simulation to support standards development. Yet technical challenges such as poor low altitude radar coverage and lack of required transponder equipage are significant barriers to characterizing the low altitude environment. In response, we conducted an extensive review of fixedwing and rotary-helicopter manned operations to characterize the low altitude environment.

/pdf/a-review-of-low-altitude-manned-aviation-operations-nehft93p7e.pdf

A Review of Low Altitude Manned Aviation Operations

Developing a collision avoidance system that can meet safety standards required of commercial aviation is challenging. A dynamic programming approach to collision avoidance has been developed to optimize and generate logics that are robust to the complex dynamics of the national airspace. The current approach represents the aircraft avoidance problem as Markov Decision Processes and independently optimizes a horizontal and vertical maneuver avoidance logics. This is a result of the current memory requirements for each logic, simply combining the logics will result in a significantly larger representation. The “curse of dimensionality” makes it computationally inefficient and infeasible to optimize this larger representation. However, existing and future collision avoidance systems have mostly defined the decision process by hand. In response, a simulation-based framework was built to better understand how each potential state quantifies the aircraft avoidance problem with regards to safety and operational components. The framework leverages recent advances in signals processing and database, while enabling the highest fidelity analysis of Monte Carlo aircraft encounter simulations to date. This framework enabled the calculation of how well each state of the decision process quantifies the collision risk and the associated memory requirements. Using this analysis, a collision avoidance logic that leverages both horizontal and vertical actions was built and optimized using this simulationbased approach.

An information theoretic approach for generating an aircraft avoidance Markov Decision Process

This document describes a set of baseline collision avoidance algorithms which can be used as a point of departure for the development of final algorithms for the FAA's Beacon-based Collision Avoidance System (BCAS). The algorithms were structured to permit great flexibility in an experimental environment such as NAFEC. They incorporate a number of selectable options in the collision avoidance logic and in the display output. One option permits the selection of either a passive mode logic or an active mode logic. When the passive mode is selected, other options allow horizontal positive or negative commands to be used. In addition, the display of positive or negative commands can be selected or suppressed, and limit vertical rate commands can be selected for display independently of positive or negative commands. Two types of Intruder Position Data (IPD)-flashing IPD's and ordinary IPD's-can also be selected for display. The logic can drive three types of cockpit displays namely, an ACAS display, a baseline IPC display, and a general purpose Plan View Display (PVD). (Author)

J Clark

Papers

Initial collision avoidance algorithms for the beacon-based collision avoidance system