Bonnie D. Allen

Prototype flight management capabilities to explore temporal RNP concepts

Next generation air transportation system (NextGen) concepts of operation may require aircraft to fly planned trajectories in four dimensions - three spatial dimensions and time. A prototype 4D flight management capability is being developed by NASA to facilitate the development of these concepts. New trajectory generation functions extend todaypsilas flight management system (FMS) capabilities that meet a single required time of arrival (RTA) to trajectory solutions that comply with multiple RTA constraints. When a solution is not possible, a constraint management capability relaxes constraints to achieve a trajectory solution that meets the most important constraints as specified by candidate NextGen concepts. New flight guidance functions provide continuous guidance to the aircraftpsilas flight control system to enable it to fly specified 4D trajectories. Guidance options developed for research investigations include a moving time window with varying tolerances that are a function of proximity to imposed constraints, and guidance that recalculates the aircraftpsilas planned trajectory as a function of the estimation of current compliance. Compliance tolerances are related to required navigation performance (RNP) through the extension of existing RNP concepts for lateral containment. A conceptual temporal RNP implementation and prototype display symbology are proposed.

Serious Gaming for Test & Evaluation of Clean-Slate (Ab Initio) National Airspace System (NAS) Designs

Incremental approaches to air transportation system development inherit current architectural constraints, which, in turn, place hard bounds on system capacity, efficiency of performance, and complexity. To enable airspace operations of the future, a clean-slate (ab initio) airspace design(s) must be considered. This ab initio National Airspace System (NAS) must be capable of accommodating increased traffic density, a broader diversity of aircraft, and on-demand mobility. System and subsystem designs should scale to accommodate the inevitable demand for airspace services that include large numbers of autonomous Unmanned Aerial Vehicles and a paradigm shift in general aviation (e.g., personal air vehicles) in addition to more traditional aerial vehicles such as commercial jetliners and weather balloons. The complex and adaptive nature of ab initio designs for the future NAS requires new approaches to validation, adding a significant physical experimentation component to analytical and simulation tools. In addition to software modeling and simulation, the ability to exercise system solutions in a flight environment will be an essential aspect of validation. The NASA Langley Research Center (LaRC) Autonomy Incubator seeks to develop a flight simulation infrastructure for ab initio modeling and simulation that assumes no specific NAS architecture and models vehicle-to-vehicle behavior to examine interactions and emergent behaviors among hundreds of intelligent aerial agents exhibiting collaborative, cooperative, coordinative, selfish, and malicious behaviors. The air transportation system of the future will be a complex adaptive system (CAS) characterized by complex and sometimes unpredictable (or unpredicted) behaviors that result from temporal and spatial interactions among large numbers of participants. A CAS not only evolves with a changing environment and adapts to it, it is closely coupled to all systems that constitute the environment. Thus, the ecosystem that contains the system and other systems evolves with the CAS as well. The effects of the emerging adaptation and co-evolution are difficult to capture with only combined mathematical and computational experimentation. Therefore, an ab initio flight simulation environment must accommodate individual vehicles, groups of self-organizing vehicles, and large-scale infrastructure behavior. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ab initio simulation environment is similar to online gaming environments in which player participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual players active participation.

A Flexible Flight Control System for Rapid GNC and Distributed Control Deployment

The NASA Langley Autonomy Incubator focuses on enabling autonomous, cooperative operations of multiple small Unmanned Aerial Vehicles (UAV) and, more generally, creating autonomous system technologies that change the way people and goods are moved from place to place. To enable rapid test and deployment of autonomous algorithms an avionics system is under development. The system, called the Autonomous Entity Operations Network Flight Control System (AEON-FCS), is being developed to be capable of implementing both classic Guidance, Navigation, and Control (GNC) law algorithms in a monolithic system architecture and also a network connected fully distributed control system. AEON-FCS is a subset of the overall AEON system that includes the Avionics System for Remotely Operated Vehicles LiTe (ASROV-LT) codebase and additional data centric tools for distributed control implementation and rapid simulation and testing. The ASROV-LT codebase provides utilities for hard real-time flight control loop processing and serialized sensor parsing. To enable rapid testing of autonomous algorithms, AEON-FCS provides seamless integration between simulation and hardware by utilizing a data centric inter-process communication approach and a global data bus available on the network. A goal for the AEON-FCS is to enable implementation of fully distributed control. Processing locations may be paired with sensors and distributed across either an airframe or across different air and/or ground vehicles on the network connected system. AEON-FCS aims to enable agile, high performance, robust operation of single or multiple cooperative UAV performing challenging missions like search and rescue under the forest canopy, delivery in rapidly changing unstructured environments (rescue in burning building), and ensemble collaboration for robust resilient system operations in hostile environments. The current state of the structure, function, and novel features of the AEON-FCS are described herein.

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments - "After all these years among humans, you still haven't learned to smile."

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilots flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Swarm Size Planning Tool for Multi-Job Type Missions

As part of swarm search and service (SSS) missions, swarms are tasked with searching an area while simultaneously servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of multiple types. Each type requires that vehicle(s) break off from the swarm and travel to the job site for a specified amount of time. The number of vehicles needed and the service time for each job type are known. Once a job has been successfully serviced, vehicles return to the swarm and are available for reallocation. When planning SSS missions, human operators are taskedwith determining the required number of vehicles needed to handle the expected job demand. The complex relationship between job type parameters makes this choice challenging. This work presents a prediction model used to estimate the swarm size necessary to achieve a given performance. User studies were conducted to determine the usefulness and ease of use of such a prediction model as an aid during mission planning. Results show that using the planning tool leads to 7x less missed area and a 50% cost reduction.

Towards an Open, Distributed Software Architecture for UxS Operations - "It's difficult to work in groups when you're omnipotent," - Q Star Trek: The Next Generation:: Deja Q (1990)

To address the growing need to evaluate, test, and certify an ever expanding ecosystem of UxS platforms in preparation of cultural integration, NASA Langley Research Centers Autonomy Incubator (AI) has taken on the challenge of developing a software framework in which UxS platforms developed by third parties can be integrated into a single system which provides evaluation and testing, mission planning and operation, and out-of-the-box autonomy and data fusion capabilities. This software framework, named AEON (Autonomous Entity Operations Network), has two main goals. The first goal is the development of a cross-platform, extensible, onboard software system that provides autonomy at the mission execution and course-planning level, a highly configurable data fusion framework sensitive to the platforms available sensor hardware, and plug-and-play compatibility with a wide array of computer systems, sensors, software, and controls hardware. The second goal is the development of a ground control system that acts as a test-bed for integration of the proposed heterogeneous fleet, and allows for complex mission planning, tracking, and debugging capabilities. The ground control system should also be highly extensible and allow plug-and-play interoperability with third party software systems. In order to achieve these goals, this paper proposes an open, distributed software architecture which utilizes at its core the Data Distribution Service (DDS) standards, established by the Object Management Group (OMG), for inter-process communication and data flow. The design decisions proposed herein leverage the advantages of existing robotics software architectures and the DDS standards to develop software that is scalable, high-performance, fault tolerant, modular, and readily interoperable with external platforms and software.

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems - “What it needs in order to evolve, is a human quality. Our capacity to leap beyond logic.” - Capt. Kirk, Star Trek: The Motion Picture

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.