Note: Unless otherwise noted, project Principal Investigator (PI) is Dr. DeLaurentis.
High Speed Flight Systems & Defense Cluster
Advanced Hypersonics Technology Sponsor: SAIC/Naval Surface Warfare Center
Synopsis: Indirect trajectory optimization can be a powerful tool in characterizing vehicle performance, design tradeoffs, and the impact of constraints in addition to specific mission planning. To enable this, this project has provided and refined a framework to quickly generate large families of high-quality optimal trajectories for hypersonic glide vehicles. Specific areas of investigation have included the relationship of control limits on reachability footprints and mission performance, the effect of variation in initial states and parameters, uncontrollability in certain regimes, and the tradeoff maximum range and terminal energy. Continued work strives to refine the vehicle model through more accurate dynamics, removal of simplifying assumptions, and better interpolation and improve indirect optimization tools and techniques for increased reliability and speed.
Study of Swarm Characteristics and Modeling Innovations Sponsor: Lockheed Martin Missiles & Fire Control
Multidisciplinary Hypersonics Program Task 4: Hypersonic Vehicle Design & Dependency Analysis Co-investigators: Dr. Byron Pipes (AAE), Dr. Jonathan Poggie (AAE), Dr. Vikas Tomar (AAE), Dr. Rod Trice (MSE) Sponsor: Air Force Research Laboratory
Synopsis: The objective of this project is to develop and demonstrate a systems analysis workbench to optimize complex design problems for hypersonic vehicles, which are subject to severe size, weight, and power (SWaP) sensitivities. In particular, we will focus on trajectory optimization tools, material ablation tests, and Extrusion Deposition Additive Manufacturing (EDAM) for composite structures
Autonomy for Hypersonics Principal-investigator: Dr. Ali K. Raz Co-investigator: Dr. Daniel DeLaurentis Sponsor: Sandia National Laboratories
Synopsis: The project is focused on the application of AI techniques for hypersonic vehicle mission planning. The objective is to investigate the use reinforcement learning (RL) for real-time guidance commands. We aim to define hypersonic mission modeling abstractions required to provide well suited means of exploratory analysis. The mission model is provided via DAF 2.0, in which abstract levels of agent capability are implemented to simulate a hypersonic flight environment. Optimal trajectories seed the RL agent for training and are used to compare against RL generated results. Trajectories generated by the autonomous hypersonic vehicle are validated, verified, and tested to quantify performance limitations of RL solutions.
Advanced Aerial Mobility & Air Transportation Cluster
Operations limits for passenger-carrying Urban Air Mobility missions Principal-investigators: Dr. Daniel DeLaurentis, Dr. William Crossley Sponsor: National Institute of Aerospace, NASA Langley Research Center
Synopsis: The convergence of new technologies, such as electric propulsion, autonomy, and new business models, such as app-based ride sharing, are generating the potential for a new aviation market known as Urban Air Mobility (UAM) to emerge. It is envisioned that UAM may revolutionize mobility within metropolitan areas by enabling a safe, efficient, convenient, affordable, and accessible air transportation system for passengers and cargo. Such an air transportation system could bring aviation into people’s daily lives and provide an augmentation and/or alternative to other ground-based transit modes, such as cars. The UAM market is not likely to appear overnight. Rather, some form of evolutionary approach based on the pace of technology development, infrastructure limitations, societal acceptance, airspace integration, and many other factors may bring us from the current state of the art to the envisioned future state where aviation is a normal part of people’s daily lives. To help NASA’s Aeronautics Research Mission Directorate (ARMD) consider potential near-term applications for passenger-carrying UAM and which issues will be the key “bottlenecks” limiting the scalability of early UAM operations, this task is focused on studying the “operational limits” of near-term UAM applications. The work builds upon previous methodology developments of the Purdue PIs to assess the mobility benefits from CTOL and VTOL operations, at a regional transportation level and with initial “hooks” into urban areas.
Enhanced methodology for exploring autonomy-enabled multi-mode regional transportation Sponsor: Center for Connected and Automated Transportation
Synopsis: Increasing the level of autonomy in both small aircraft and automobile has the potential to generate greater efficiency and utility in multimodal regional transportation systems. In previous research, the PI and collaborators developed a computational analysis framework to assess the impact of aircraft technology advancement in electric propulsion and autonomy on the future of on-demand, regional air transportation system. A sensitivity analysis revealed increasing level of autonomy and an improved ride-sharing model (on the ground and in the air) could lead to significant increase in the total number of individuals who could afford this new mode of transportation. Activities in this project would enhance our current computational framework with models for autonomous automobile option and thereby take a holistic approach to evaluate the impact of autonomy at a multi-modal level of operation. The end results will help identify the promising regions, via an optimization formulation, where enabling autonomy makes economic sense to the stakeholders. Outcome models, analysis, and metrics is expected to further increase the research community’s ability to characterize the impacts of differing levels of autonomy as well as the synergistic benefit of a ride-sharing economy within the context of a multi-modal transportation system
Secure and Safe Assured Autonomy (S2A2) Sponsor:NASA University Leadership Initiative
Synopsis: The recent introduction of unmanned systems into the NAS will bring challenges and opportunities for the nation’s aviation system. The integration of such a complex transportation system creates a clear need to develop new technologies and innovative operational concepts for secure and safe assured autonomy. An unmanned systems future will see the integration of a wide variety of Unmanned Aerial Systems (UAS), personal air vehicles, Urban Air Mobility (UAM) vehicles, and cargo and special mission aircraft into the NAS. These developments can leverage UAS Traffic Management (UTM) advancements for the unique requirements of UAM airspace management. The main challenges include: (i) sensing and understanding complex operational surroundings, coordinating different types of aerial vehicles, planning and navigation through highly dynamic and uncertain environments; (ii) securing the NAS against a wide range of malicious adversarial threats, specifically cyber-physical attacks; (iii) verifying and validating autonomous system operation; and, finally, (iv) properly integrating new vehicles and traffic management approaches in the midst of autonomy. Our primary goal is to ensure safe, secure and robust integration of autonomous vehicles into a UAM-tailored transportation infrastructure while maintaining compliance with existing commercial and civil air transportation safety standards.
Traffic Information Exchange Network (TIEN) Sponsor: NASA University Student Research Challenge
Synopsis: Traffic Information Exchange Network is a relay-based broadcasting protocol that can enable data sharing between multiple devices. The protocol works similarly to “gossip protocol” where a device keeps listening to the neighboring broadcasted messages and whenever it receives a message, it adds its own information on top of the old one and broadcasts the new message. This mechanism enables two distant devices to share information with each other through a device in between. This is especially important in an urban environment where information between two relatively closer drones can be blocked due to obstacles, such as skyscrapers. Relay mechanism ensures that the critical information is received if reasonable density of the devices in the area is maintained. Currently the focus is on the development and testing of a secured data relay system for UAVs. As part of this effort, the data-relay capability and performance of TIEN in a dynamic traffic environment, has been characterized using an in-house simulation environment. Additionally, our group successfully implemented the protocol on a prototype hardware and performed proof-of-concept experiments, on Purdue campus, to demonstrate the relay mechanism. This project intends to enhance TIEN capability and demonstrate its cybersecurity mechanism. There are three distinct tasks to validate the potential of TIEN: (i) investigating and enhancing the TIEN’s cyber-security capabilities, (ii) identification of path to integrate TIEN with other airspace management systems, and (iii) improving vehicle localization accuracy in urban environment using TIEN communication properties.
Data-driven monitoring framework for terminal airspace Sponsor: Robust Analytics
Synopsis: The objective of the project is to develop predictive models and metrics for the terminal airspace to predict safety margins and operational states for the airspace. The ability to tightly integrate data-driven analytics into airspace operations is paramount for the transition from current legacy systems to flexible systems that can incorporate UAV traffic, future autonomous systems We leverage aviation data sources (e.g., Traffic Flow Management System (TFMS), Notice to Airmen (NOTAMs), weather data, etc.) to develop airspace safety models and supporting analytical tools for flight track prediction, anomaly identification (e.g., go arounds, holding patterns), and operational changes.
Aircraft Technology Modeling and Assessment - NextGen Supersonic Fleet evaluation Principal-investigator: Dr. William Crossley Co-investigator: Dr. Daniel DeLaurentis Sponsor: FAA ASCENT Center of Excellence
Synopsis: The project is focused on developing a model that measures fleet-wide environmental impact from new aircraft concepts and technologies under various carbon policy scenarios, based on an approach that mimics airline behavior. Fleet-level Environmental Evaluation Tool (FLEET) considers uses a "system dynamics-like" approach to allow demand, fleet size/composition, and fares to evolve over time while considering scenarios with varying technological, policy, & economic factors. The current focus is a collaborative effort between Georgia Institute of Technology and Purdue University to leverage capabilities and knowledge available from the multiple entities that make up the ASCENT university partners and advisory committee. Purdue's primary directive for this research project is to support the Federal Aviation Administration (FAA) in modeling and assessing the potential future evolution of the next-generation supersonic aircraft fleet. Purdue's research under this project consists of three integrated focus areas: (a) establishing fleet assumptions and performing demand assessment; (b) performing preliminary SST environmental impact prediction; (c) performing vehicle and fleet assessments of potential future supersonic aircraft. More details are available at the project website.
Space Systems Cluster
Modeling Architectures and Parametrization for Spacecraft (MAPS) Internal project
Synopsis: The Modeling Architectures and Parametrization for Spacecraft (MAPS) Environment was constructed in-house by students. The environment, coded in MATLAB, enables non-traditional analysis of spacecraft and space architectures. Multiple scenarios and architecture designs can be evaluated, and users can perform trade studies based on several metrics, including cost and complexity. Additional tools enable extensibility, repeatability, and decision support. The object-oriented nature of the environment allows the SoS group to continue developing the environment. Current work is underway to import information directly from SysML models into the environment.
Performance Measures for Autonomous Intelligent Agents in Satellite Systems (PEAS) Principal-investigators: Dr. Jitesh Panchal (Purdue), Dr. Laura Freeman (Virginia Polytechnic Institute & State University) Co-investigators: Dr. Daniel DeLaurentis Sponsor: National Reconnaissance Office
Synopsis: As space assets become more intelligent, it is imperative that we are able to evaluate their performance accurately. The Performance Measures for Autonomous Intelligent Agents in Satellite Systems (PEAS) is a collaborative effort between Purdue University and Virginia Polytechnic Institute and State University to establish a framework to measure the performance of Autonomous Intelligent Agents (AIAs) in spacecraft for the National Reconnaissance office (NRO). In the past year, the team molded a constellation of Earth observing satellites and the intelligent agents that control them. Then, the team developed performance measures that include typical metrics used to assess spacecraft, such as mass and propellant consumption, in addition to measures that evaluate intelligent collaborative systems,such as degree of trust. Future efforts involve evaluating performance measures for complex teams of spacecraft
System of Systems /Systems Engineering Methodology Cluster
Learn 2 Gamebreak (L2G) Co-investigators: Dr. Ali K. Raz (AAE), Dr. Jitesh Panchal (ME) Sponsor: DARPA
Synopsis: The L2G project seeks to develop and apply Artificial Intelligence (AI) techniques to existing open-world video games to quantitatively assess game balance, identify parameters that significantly contribute to the game balance and explore new capabilities, tactics, and rule modifications that are most destabilizing to the game. For Gamebreaker, game balance is defined as an inherent property of the game that reflects the win/loss ratio of players of equal skill level based on strategies and tactics employed within the game. For example, in a balanced game, if the skill level of both players is equal, each player will win 50 percent of the total games played. Similarly, if the skill level of both players is equal, but one player wins disproportionately due to inherent advantages arising from a condition of the game construct, the game is unbalanced. The commercial gaming industry has a long-standing interest in maintaining game balance since balanced games are more entertaining, and market pressures drive their development. Contrary to these market goals, strategists in the military deliberately explore technologies that maximize imbalance to increase the probability of their winning.
Quantitative Dynamic Planning in Human-in-the-loop Multi Agent Systems Principal-investigator: Dr. Shaoshuai Mou Co-investigators: Dr. Daniel DeLaurentis (Purdue), Dr. Joydeep Biswas (UT Austin), Dr. Bing Liu (UIC) Sponsor: Northrop Grumman (REALM)
Synopsis: Rescue teams descended on the destruction left by Hurricane Michael in October, frantically searching for survivors. But a week later, more than 1,000 people were still unaccounted for, leaving families to wait and hope. Drone assistance in natural disaster response now is simplistic at best with a number of hurdles. The main focus is to use artificial intelligence and learning algorithms to create a platform allowing multiple drones to communicate and adapt as mission factors change. In this research, AI and machine learning techniques will assist the system in many ways, such as in object recognition and human-machine communication, and improving the system performance over time. Especially the system assisted by AI will allow for input from a human commander into the mission parameters and lets the drones provide feedback and even suggestions in natural language. Distributed control, human-machine mixed autonomy, life-long learning and artificial intelligence will be the key enablers.
Predictive and Prescriptive Methods for System-of-Systems Acquisition Sponsor: Naval Postgraduate School
Synopsis: System-of-Systems capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. The systems within an SoS serve two purposes: one is to meet their own independent objectives, and the second is to contribute some capability to the SoS from which all constituents can benefit. In recent decades, the fields of machine learning and data analytics have found widespread application in system design and acquisitions. It is unanimously understood that any organization acquiring a complex system employs some form of data analytics to assess a system’s independent objectives. Even Acquisition Research Program: Creating Synergy for Informed Change - 2 - though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of this work is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. We propose to utilize machine learning techniques to predict the SoS capability by sharing pertinent datasets and prescribe the information links between systems to enable this sharing.
Production Engineering Education and Research (PEER) Principal-investigators: Dr. Audeen Fentiman (Engineer Education) Co-investigators: Dr. Kerrie Douglas (Engineering Education), Dr. Jorge Camba (Purdue polytechnic institute), Dr. John Sutherland (Environmental and Ecological Engineering),Dr. Daniel DeLaurentis Sponsor: NSF EHR Core Research, Boeing
Synopsis: Production Engineering Education and Research (PEER) aims to accelerate training in critical skill areas for the Nation's engineering and advanced manufacturing workforce. The project at Purdue focuses on the development of online modular courses on MBSE for working professionals, 4-year, and 2-year university students. It is a joint project with faculties of Engineering Education, Environmental and Ecological Engineering, Purdue Polytechnic Institute, College of Education, Purdue Online.