Optimal Recovery Trajectories for Automatic Ground Collision Avoidance Systems (Auto GCAS)

The USAF\u27s F-16 Automatic Ground Collision Avoidance System (Auto GCAS) uses a single pre-planned roll to wings- level and 5-g pull-up to meet the operational requirements of being both aggressive and timely, meaning that extremely agile avoidance maneuvers will be executed at the last second to avoid the ground. There currently exists no similar Auto GCAS for manned military heavy\u27 aircraft with lower climb performance such as transport, tanker, or bomber aircraft. This research proposes a new optimal control approach to the ground collision avoidance problem for heavy aircraft by mapping the aggressive and timely requirements of the automatic recovery to an optimal control formulation which includes lateral maneuvers around terrain. Results are presented for representative heavy aircraft scenarios against 3-D digital terrain, which are then compared to a Multi-Trajectory Auto GCAS with five pre-planned maneuvers. Metrics were developed to quantify the improvement from using an optimal approach versus the pre-planned maneuvers. The research results provide a basis to evaluate the expected performance of any future Auto GCAS for all aircraft

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

Multi-Path Automatic Ground Collision Avoidance System for Performance Limited Aircraft with Flight Tests: Project Have Medusa

A multi-path automatic ground collision avoidance system (Auto-GCAS) for performance limited aircraft was further developed and improved to prevent controlled flight into terrain. This research includes flight test results from the United States Test Pilot School\u27s Test Management Project (TMP) titled Have Multi-Path Escape Decisions Using Sophisticated Algorithms (MEDUSA). Currently, the bomber and mobility air- craft communities lack an Auto-GCAS. The F-16 Auto-GCAS was proven successful for fighter-type aircraft with seven aircraft and eight lives saved from 2014 to 2018. The newly developed and tested Rapidly Selectable Escape Trajectory (RSET) sys- tem included a 5-path implementation which continuously updated at a rate of up to 12.5 Hz. The research employed Level 1 Digital Terrain Elevation Data (DTED) to identify the offending terrain and an augmented 6 Degree-of-Freedom (DoF) Stitched aerodynamic model to create terrain avoidance paths based on the aircraft\u27s current state and location. The system then triggered when all paths predicted collision with the DTED and automatically activated the path which had the longest time until impact. A terrain safety buffer (TSB) of 200 ft added to the DTED to allowed for the time needed to process and execute the maneuver. The RSET system was flight tested against DTED using the Calspan Learjet 25D Variable Stability System (VSS). Path prediction error (PPE) did not meet the specified criteria and was larger than expected for the 30-second path predictions; however, at the maximum refresh rate of 12.5 Hz, the RSET system ensured terrain clearance in all cases tested. The RSET system was able to achieve and maintain target load factor and flight path angle with momentary overshoots. The system showed no tendency for nuisance. The RSET hand-back was favorable and can be used as a baseline for future Auto-GCASs

Multi-Trajectory Automatic Ground Collision Avoidance System with Flight Tests (Project Have ESCAPE)

Multi-trajectory automatic collision avoidance techniques for heavy-type aircraft are explored to increase aviation safety procedures and decrease losses due to controlled flight into terrain. Additionally, this research includes flight test results from the United States Test Pilot School’s Test Management Project (TMP) titled Have Emergency Safe Calculated Autonomous Preplanned Exit (ESCAPE). Currently, the heavy aircraft community lacks an automatic collision avoidance system that has proven to save lives in fighter-type aircraft. The tested algorithm includes both a 3-path and a 5-path avoidance technique that is compared to an optimal solution which minimizes aircraft control to avoid terrain. The research utilizes Level 1 Digital Terrain Elevation Data (DTED) to analyze the terrain and a 3-Degrees of Freedom (DOF) Equations of Motion (EOM) model to predict potential terrain avoidance paths for the aircraft based on current location. The algorithm then waits until all paths collide and automatically activates the path with the longest time until collision with an appropriate time safety margin. The research also characterizes terrain based on changing slope and presents a new classification of aircraft based on performance capabilities. The result was used for algorithm parameter specification of path execution times and pre-planned maneuver creation so that the system can be modified for a wide variety of aircraft. Finally, the algorithm was flight tested against DTED in a simulated environment using the Calspan Learjet to determine actual 3 and 5- path performance, parameter specification, and comparison to the optimal solution. The important recommendations include a need for flexible entry parameters based on current aircraft state, continued evaluation of the terrain during avoidance maneuver execution, and more precise control of the aircraft flight path angle. Finally, due to comparison with the optimal solution, it is concluded that an acceptable terrain avoidance algorithm is possible using only a 3-path solution given that all three paths include a climbing maneuver

Flight Deck Automation Support with Dynamic 4D Trajectory Management for ACAS: AUTOFLY-AID

AUTOFLY-Aid Project aims to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. The main theoretical innovative and novel concepts to be developed by AUTOFLY-Aid Project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck’s perspective, as seen/measured/informed by the aircraft flying in SESAR 2020 b) design and development of a dynamic trajectory planning algorithm that can generate at real-time (on the order of seconds) flyable (i.e. dynamically and performance-wise feasible)alternative trajectories across the evolving stochastic composite airspace picture (which includes new conflicts, blunder risks, terrain and weather limitations) and c) development and testing of the Collision Avoidance Automation Support System on a Boeing 737 NG FNPT II Flight Simulator with synthetic vision and reality augmentation while providing the flight crew with quantified and visual understanding of collision risks in terms of time and directions and countermeasures

Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict

Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo delivery drones will require on-demand scheduling of large numbers of aircraft. We examine the scalability of an algorithm known as FastMDP which was shown to perform well in deconflicting many dozens of aircraft in a dense airspace environment with terrain. We show that the algorithm can adapted to perform first-come-first-served pre-departure flight plan scheduling where conflict free flight plans are generated on demand. We demonstrate a parallelized implementation of the algorithm on a Graphics Processor Unit (GPU) which we term FastMDP-GPU and show the level of performance and scaling that can be achieved. Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher. We believe the results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both a centralized or distributed flight planning syste