ATM (Air Traffic Management) Technology Demonstration (ATD)-1 challenge develops and delivers integrated aircraft-based and ground-based automation technologies to the FAA NextGen and Air Traffic Organizations, the FAA Surveillance Based Systems Program Office, and flight operators, to enable improved arrival operations efficiency while increasing arrival throughput.

ProtoInnovations, LLC (PI) will research, design, develop, and validate advanced locomotion controls, rover-based non-prehensile manipulation (RNM) actions, and novel hardware/software architectures to allow rovers to alter the environment around them for the purposes of improving terrainability, aiding in scientific investigations, and accomplishing construction tasks. This work will require the development of analytical models for different rover configurations and different terrains. These models will give insight into the RNM capabilities of current NASA rover configurations, design considerations for future NASA rover configurations, and requirements for controllable RNM actions. Useful RNM actions will also be explored by considering the impact on NASA missions as well as their feasibility on current NASA rovers. Control strategies will then stem from analytical model research and RNM action definitions. Locomotion controls verification and validation will be done in simulation and on real NASA rovers in the field.

Phase I will involve the research and development of the analytical models that inform RNM actions, control architecture conceptualization, and the implementation of a set of RNM actions both in simulation and on at least one NASA rover. Meeting these objectives will form deliverables that directly benefit NASA as well as mark significant progress in the overall project objective of enabling RNM actions for improved mobility, better scientific investigations, and new rover functions.

As computer architecture becomes more parallel, numerical simulations must follow suit and exploit parallel algorithms effectively. An exascale-capable computational fluid dynamic method using Lattice Boltzmann Method (LBM) has been proposed to simulate compressible flow. The method is embarrassingly parallel which allows the method to fully utilize parallel architectures such as Graphics Processing Units (GPUs). The method improves upon previous methods and allows for variable fluid properties including specific heat ratio and Prandtl number. In addition, the method increases the traditional Mach number limit of LBM from 0.2 to 3.0 allowing for the method to simulate transonic and supersonic phenomena. The Phase I project will investigate the accuracy and speed of the method with respect to existing NASA solvers including NASA OVERFLOW and NASA FUN3D. The LBM solver will be written in serial and in parallel using NVIDIA’s CUDA to allow for GPU use. Future work is discussed to improve upon the method and to incorporate the method into NASA solvers such as NASA LAVA and NASA Cart3D.

Current state of the art inertial measurement units (IMUs) co-locate a set of accelerometers and gyroscopes into a single package. CU Aerospace (CUA), in partnership with the University of Illinois, propose to develop a scalable and distributed IMU for space robotics and CubeSat applications. The user can choose to include an arbitrary number of inertial sensors beyond the minimal number of sensors required for inertial navigation (3 gyroscopes and 3 accelerometers). This scalability enables both improved measurement resolution and system redundancy. The distributed nature of the system means that sensors can be placed arbitrarily by the user as needed in their design, under the constraint that each axis is measured by at least one accelerometer and gyroscope. This technology enables space-constrained systems to leverage redundant inertial sensors for fault detection and isolation (FDI). Beyond the systems engineering benefits of this system, distributing the sensors is grounded by previous research that suggests it will reduce the total noise of its output measurements. This technology can potentially be used in most robotic systems currently using an inertial navigation system. However, the best applications of this technology are in space constrained robots that can benefit from accurate state estimates or fault tolerant systems.

MAESTRO (Management of manned spacecraft operations through intelligent, AdaptivE, autonomouS, faulT identification and diagnosis, Reconfiguration/replanning/rescheduling Optimization) substantially leverages previous NASA investments to assemble the correct set of technologies to implement all aspects of the intelligent, semi-autonomous spacecraft operations manager.  We have significant experience in all of the required technologies and have already integrated them into a general MAESTRO architecture designed to be easily applied to all spacecraft subsystems.

The eventual, ultimate goal is the ability of astronauts and a semi-autonomous, intelligent onboard system to easily manage all spacecraft operations through the development of MAESTRO, which can easily interface to the various systems of a variety of spacecraft. MAESTRO must be sufficiently powerful, general, and computationally efficient and be easily adapted by developers.  This will be accomplished using open standards, clearly defined open interfaces, use of Open Source software, and leveraging several previous NASA investments. 

The Phase I research goals are to explore the various spacecraft subsystem domains, elaborate the AI techniques useful for subsystem characterization, diagnosis, and replanning/rescheduling/adaptive execution/safing, prove the feasibility of these techniques through prototype development, and develop a complete system specification for the Phase II MAESTRO system.

There is substantial evidence suggesting that a Lithium-ion cell undergoes internal structural and mechanical changes prior to a catastrophic failure. Some of these changes include electrode expansion, electrode ruffling, dendrite formation, internal gas formation, and internal density changes. A key characteristic of these changes is that most of them occur prior to any external measurable parameter variation, such as in terminal voltage, surface temperature, or mechanical surface strain. Therefore, detecting internal cell structural and mechanical changes early and with adequate resolution has several benefits, including the prevention of catastrophic accidents sufficiently ahead of time, and the gathering of additional information that can be used to more accurately assess the health and life of cells during operation. We propose a novel approach that simultaneously detects and corrects these internal cell changes early and using hardware that can be permanently installed externally on the surface of a lithium-ion cell. Our approach enhances the safety and prognostics associated with lithium-ion batteries, and its reconstruction capability has the added benefit of rejuvenating a cell to extend its life. Finally, the proposed solution will be implemented on small, low cost, and low power hardware to ensure its seamless integration to existing commercial cells and systems.