Research Topics

The ability to measure celestial objects across different spectral regions, using advanced imaging processing techniques, utilizing ultrasensitive detection techniques, and/or leveraging adaptive optics can enable significant increases in observational capacity and ultimately better positional accuracies. We seek to foster several different modalities that can improve the imaging of celestial objects (including those in Earth's orbit). Some questions that we are interested in are:

• How can non-centrosymmetric optics be used to improve compact telescope designs (particularly by removing obscurants)?
• Can size-, weight-, and power-constrained focal plane arrays be built that examine multiple spectral regions? Can multiple focal plane arrays be used in highly limited form factors to increase spectral ranges?
• Are there novel methods for sensing and correcting incoming light through highly degrading environments or rapidly changing conditions?
• How can the field of view be maximized while limiting or eliminating aberrations (spherical, chromatic, etc.)? Are there techniques, methods, or processes for achieving very high fields of view?

MEMS sensors have achieved success in commercial and other high-volume applications. We are seeking to use MEMS technologies for precision applications. We are interested in advancements in the following areas:

• Development of high-performance (~0.01 deg/hr) MEMS gyro.
• Development of micro-scale hemispheric resonator gyros using novel fabrication techniques.
• Development of discrete-mass, in-plane oscillator gyroscopes.
• New MEMS gravimeter designs that outperform existing mechanical gravimeters.
• Packaging and mounting technologies for MEMS sensors where mounting stresses in MEMS technologies are minimized.

A key challenge for APNT is distinguishing critical data from meaningless data. Traditional sensors accept all data that surpasses their detectability limits, which means that they are always on (power hungry) and are constantly outputting data (which necessitates processing capabilities).

The use of new methods, such as AI/ML, neuromorphic sensors, sparse sampling, and multi-hub/multi-node webs can help mitigate this data avalanche at the edge of the sensor/phenomena interface. We seek insights into the following questions:

• How can we use edge sensing/data fusion methods to reduce sensor power consumption and sensor failure rates?
• Can we use neuromorphic sensing methods to significantly evolve/change sensor functionality and operation without human intervention?
• For a given detection system, can we significantly improve processing capacity but still maintain overall detection capability by reducing irrelevant sensor outputs?

Atomic and molecular systems provide a set of highly precise and accessible energy levels that can be used for precision timing. Some questions we are seeking insight into are:

• How can we reduce the size of chip-scale atomic clocks? Can we move to higher frequencies (e.g., terahertz) for intra-atomic coupling?
• How can we rapidly and accurately synchronize multiple independent clocks that are spatially separated?
• Beyond the commonly used systems, what other atomic or molecular gases have the potential to achieve very high timing precision? Can the ancillary support structures also be miniaturized and packaged?

We seek advancements in atomic and molecular sensing of electric and magnetic fields, such as:

• Use Rydberg atoms to simultaneously sense electric (RF) and magnetic fields. Can detectability be increased? Can the detection be shown to be vectorized?
• Atomic gravimetry for precision sensing of gravity in highly constrained (size, weight, and power) packages and/or unstable platforms (e.g., moving vehicles, airplanes, ships, etc.).
• Use atomic or molecular gases for very high precision magnetic field detection (magnitude or vectorized).
• Low-dimensional materials for sensing acoustics (air or underwater) at very specific frequencies (narrowband) and very high detectability.
• Ionic, atomic, dopant, or vacancies in materials that can be used for magnetic field sensing, such as nitrogen vacancies in diamond, dopants or inclusions in optical fiber for Faraday rotation detection, or magnetic dopants in quantum dots.

A new generation of materials has shown that when the material size becomes similar to or less than the size of relevant quantum wavefunctions, new capabilities and novel physics can be achieved. We seek a greater understanding of how room-temperature phenomena in these materials can be used in chip-scale packaging for very sensitive detection or manipulation of electric, magnetic, acoustic, optical, or quantum behaviors. Topics may include:

• Spontaneous self-assembly of materials to form a simple device or sensor.
• High-performance materials (e.g., graphene, topological insulators, phase-change materials, etc.) that can be used for sensors that manipulate light.
• Electrical control of light-matter interactions, such as rapid control over indices of refraction, high-speed manipulation of reflection/absorption/transmission, or coherent energy transfer to/from incoming light in a narrowband frequency region.
• Materials that can be used for novel acoustic sensors.

The use of precision-controlled light using unique geometries and tailored refractive indices enables a radical reduction in the size, weight, and power across the APNT spectrum. We seek to explore topics in photonics such as:

• Squeezed light for improvements in accelerometry, imaging, or sensing.
• Ultrasensitive and size-reduced sensing of rotations and linear movements in photonics.
• The use of topological photonics for novel APNT solutions.
• Flat optics for precision imaging and/or optical control.
• Photonics for observing through degraded optical environments or unique spectral regions.
• Photonics for detecting, sensing, or measuring vibrations and acoustic perturbations.

As autonomous vehicles proliferate, mission designs gravitate towards multiple autonomous agents working in cooperation to solve problems. We seek frameworks applied to multiple agents that do not merely enhance single-vehicle capabilities, but additionally, enable new mission designs and revolutionary advances in autonomous team achievements.

In large and complex engagements, many open questions remain when incorporating autonomous agents with operators, decision-makers, and planners. Some questions we are seeking insight into are:

• What level of centralization vs. decentralization in command and control is optimal in an engagement? How does this change as the environment, enemy, and resources evolve?
• What level of autonomy should be given to agents in a large operation? How does this change as the environment, enemy, and resources evolve?
• What is the optimal teaming formulation (crewed, uncrewed, crewed-uncrewed) to complete an objective? How does this change as the environment, enemy, and resources evolve? How is the trade-off between completing this mission vs. reserving resources for future engagements made?

There is a push by governments and the space industry to increase the amount of verifiable autonomy used in orbit operations and extra-terrestrial exploration. We seek advancements in spacecraft autonomy that can apply to Earth orbit, the moon, and beyond.

Robust navigation underpins higher-level autonomy functions. Our team seeks vehicle navigation and perception technologies that operate robustly both with and without GPS across domains from underwater to beyond Earth orbit.

We seek planning and decision-making frameworks that have an awareness of system uncertainty and can present operators with optimal plans including knowledge of uncertainty before and after planned actions.

Although our team has been successful in fielding autonomous systems across domains, we are constantly seeking ways to decrease the development and fielding times while increasing our robustification, continuous testing, and V&V capabilities. To meet the increasing performance guarantees required by these advanced autonomous systems and our customers, we are seeking research Ph.D. and MS-level research in simulation, V&V, automated field testing, and enabling advanced autonomy architectures.

Even though there have been significant advances in AI-based autonomy over the past decade, there are still areas that warrant additional research to increase the field-worthiness of these proposed approaches. Specifically, our team seeks AI research in the sub-fields of mission planning, explain-ability and robustness measures, AI-based planning and battlefield estimation heuristics, GN&C, and swarm tactics.

Infectious diseases are one of the largest threats to global public health. Factors such as climate change, increased population growth, expansion into animal habitats, and prevalence of antimicrobial resistance have led to the emergence of novel pathogens as well as the rise of previously controlled infections. The ability to rapidly monitor the spread of disease is crucial for prevention, interference, and control. With the emergence of SARS-CoV-2, wastewater surveillance has shown to be a useful rapid approach for monitoring disease spread and levels in the community. Additional pathogens are detectable in wastewater, allowing for the monitoring of multiple circulating pathogens. The principles of wastewater biosurveillance can extend beyond the public to government agencies to help monitor safety and threats against key assets and locations in addition to other complex environmental samples. The development of rapid pathogen agnostic detection is necessary to protect communities against and prevent the spread of novel infectious threats.

Infectious diseases are one of the largest threats to global public health. Factors such as climate change, increased population growth, expansion into animal habitats, and prevalence of antimicrobial resistance have led to the emergence of novel pathogens as well as the rise of previously controlled infections. The ability to rapidly monitor the spread of disease is crucial for prevention, interference, and control. With the emergence of SARS-CoV-2, wastewater surveillance has shown to be a useful rapid approach for monitoring disease spread and levels in the community. Additional pathogens are detectable in wastewater, allowing for the monitoring of multiple circulating pathogens. The principles of wastewater biosurveillance can extend beyond the public to government agencies to help monitor safety and threats against key assets and locations in addition to other complex environmental samples. The development of rapid pathogen agnostic detection is necessary to protect communities against and prevent the spread of novel infectious threats:

1. The development of new organ models beyond our current portfolio, particularly in neural and cardiac models.
2. The integration of immune components into organ models.
3. The development of new disease models such as for thrombosis and coagulopathy applications.
4. Automation of downstream assays via innovations in microfluidics and related domains is also key to driving down the costs and increasing the throughput of these model systems. Advancements in organ-on-chip technologies will enable rapid response to emerging threats and improve medical countermeasure development.

Modeling emerging threats and developing mitigation strategies requires an ever-advancing set of capabilities for analyzing data obtained from clinical samples, preclinical animal studies, and model systems such as organs on chips. Conventional analytical tools provide a limited window into the dynamics of pathogenesis, including entry, replication, and immune downregulation, resulting in a dearth of countermeasures despite years of research. Novel capabilities in single-cell analysis and in capturing and probing multi-omics datasets including proteomics/genomics, epigenomics, and the microbiome, will be critical in designing increasingly complex and powerful model systems for the investigation of disease mechanisms and evaluation of therapeutic approaches. Integration of these multi-omic readouts at both the tissue and single-cell levels will ultimately accelerate response to emerging threats, reduced costs, and wider availability of vaccines and therapeutics during health emergencies.

This research area covers security mechanisms, along with the associated compositional aspects, to protect the entire compute stack. Contending with strong, nation-state adversaries, our solutions must withstand the most sophisticated of attacks. Thus, our team is interested in collaborations spanning a wide range of topics including:

• Secure processor design that includes methods to (formally) verify the (generated) hardware and its security properties (i.e., lack of side channel leakage, integrity and confidentiality of the computation, and reverse engineering and Fault Injection protections).
• Secure software stack design that includes secure firmware, operating systems and languages, property-based fuzzing, compiler transformations to enforce security policies, etc.

Our team applies a diverse range of formal methods to understand and modify programs (in source code or binary form). Our analyses include static, dynamic, and hybrid approaches. In addition to scaling and extending existing approaches, we are interested in creating languages and tool interfaces to make these analyses useful for others. Research topics of interest include:

• Specification composition/synthesis, proof automation (i.e., in Coq, Agda, etc.), counterexample-guided inductive synthesis.
• Secure compilation, sound decompilation, weakest precondition analysis, abduction inference, and abstract interpretation.
• Mathematical topics (e.g., type theory, homotopy, category theory, program logics), hyperproperties, datalog/e-graphs.

This research domain covers a broad area of offensive techniques in both the hardware and software layers. Specific areas of collaboration include:

• Reverse engineering and vulnerability research approaches and tool development, focused on cyber-physical systems.
• Novel hypervisor development for code protection, instrumentation, and code obfuscation techniques.
• Research into defeating hardware-based software protections within IoT/Embedded systems.
• Compiler-based techniques include the automatic generation of exploits based on X-oriented programming, transformations to increase diversity and obfuscation (i.e., static and dynamic opaque predicates, etc.), taint analysis, control follow analysis, etc.
• Operating systems exploitation including process injection, packer techniques, networking stack, etc.
• Analog-based attacks (i.e., RF, acoustic, power, etc.) and physical attacks
• AI-driven exploit campaigns, AI poisoning, etc.

This recently established research domain takes a multidisciplinary approach to solving tough problems in cyber by harnessing the power of machine learning and artificial intelligence. Research topics plan to include:

• Building machine learning models that have the power to identify adversarial techniques tactics and procedures (TTPs) in network traffic, executables, and source code.
• Delivering cyber security to the edge via AI; this includes techniques for hardening IoT devices, military hardware and software, and space systems that automatically adapt to protect high-value assets.
• Leveraging LLMs to aid in the analysis of firmware, binaries, and malware, helping to alleviate some of the manual labor for reverse engineers.

We seek advanced methods of electromechanical design that challenge the status quo. We would like to apply innovative design methods that have the potential to achieve extreme performance, ultra-low SWAP, and/or ruggedized operation in harsh environments (e.g. Long-duration Space, Hypersonics).

Example areas might include:

1. Minute deformation in extreme thermal environments
2. Novel, low-SWAP electro-mechanical sensors
3. MEMS Stirling engine

We may share specifics details once the collaborative research topic has been agreed on.

Visual Communication of a complex, multi-dimensional, interrelated design space has proven very effective in improving design decisions and accelerating the design process. We are seeking novel methods and approaches for visualization of the design space, as well as tools and methods for exploring the tradespace within designs. Specifics of particular interests will be shared once the collaborative research topic has been agreed on.

Our team frequently works on complex engineering design problems that require tradeoffs and optimization across many criteria and constraints. Generative design algorithms can help engineers navigate these large trade spaces by automatically creating and optimizing diverse designs that can outperform manually designed systems across many variables (e.g. size, weight, power, materials, lifetime, etc.). We are seeking novel approaches to AI-driven generative design to increase performance, improve efficiency, or reduce time to manufacture in relevant engineering domains such as electro-mechanical systems, micro-electronics, and bioengineering.

Complex human state attributes (e.g., stress, fatigue) can be difficult to quantify robustly and reliably, varying widely both within and across individuals and contexts. Our team is looking for novel analytic approaches that make use of a variety of multimodal data to generate meaningful and reliable metrics of human state attributes.

Data from wearable and non-contact sensors can be sparse and of poor quality. We are looking for novel approaches to combat these challenges and maximize the information that can be obtained from these signals. Both signal process and machine learning approaches are of interest.

Commercially available devices (e.g., Fitbit, Apple watch) are commonly used to collect data and make inferences about health and state. Our experts are looking for approaches that leverage these types of wearable devices to collect more nuanced metrics (e.g., beyond gross heart rate) and other types of analogous Internet of Things devices to understand human state and intent. We are particularly interested in ideas and approaches for ‘in-the-wild’ (i.e., not in a controlled lab setting) data collection approaches and metrics to understand and make appropriate use of the context in which the data are collected.

Our Hypersonic Vehicle Technology interests are pictorially shown in Figure 1.

1. EO & RF Sensor: Sensors that can localize potential targets. The sensor includes targeting algorithms
2. Tx/RX Radio: Transmit and receive radio that can receive and send friendly messages or waveforms and/or be used for electronic warfare purposes
3. RF Antennas: RF antennas enable receipt or transmission of RF signals. Omnidirectional, beam nulling, and/or beam steering with applicable clutter and noise rejection technologies
4. Alternate Power Sources: Safe, high-power-density sources
5. Miniature actuators that can be employed to affect aerodynamic control either locally or globally

Our Hypersonic Propulsion Technology interests are pictorially shown in Figure 2.

1. Control Algorithms: Guidance and control algorithms that enable hypersonic propulsion flight while minimizing resulting stresses on the airframe and/or minimizing propulsion energy losses
2. Imbedded micro/nano sensors: sensors embedded in the structure and/or propellant to enable a better understanding of the system aging and potential resulting degradation
3. Miniature actuators that can be employed to affect aerodynamic control either locally or globally

Our Hypersonic Command and Control Technology interests are pictorially shown in Figure 3.

1. Detection and Tracking Algorithms: Algorithms that enable detection and tracking of high-speed flight vehicles using current and future sensors and sensor modalities, data processing techniques (including AI/ML)
2. C2 Decision Aids: Decision aid technologies that enable processing of detection, tracking and targeting signals, target typing and data assimilation such that human leadership can quickly absorb incoming information and make actionable plans and courses of action. This includes hardware and software, display approaches, display setups, data aggregation techniques, automated information processing, etc.

Space probes and other high-reliability systems need power in remote, harsh environments that require novel materials development to meet growing power and thermal management requirements while still maintaining small form factors. Today there are no micro power systems that have efficiencies greater than ~2% which meet all mechanical, thermal, and system requirements for small form factor power needs. Other areas of interest include novel materials and architectures that have the potential to enable new energy and power systems that achieve extreme performance, ultra-low SWAP, and/or ruggedized operation in harsh environments. Example areas might include:

1. Microelectronic coolers
2. High-temperature thermoelectric materials
3. Radiation and neutron-based power systems
4. Battery development
5. Piezoenergy transfer systems
6. Supercapacitors

Our experts may share specifics once the collaborative research topic has been agreed to.

Rapidly printing 3D structures enables new applications across a wide variety of fields biological systems, communication systems, and various electronic systems. These fields require techniques that enable at least one or more of the example areas below

1. Print smaller structures
2. Print performance materials
3. Conformal
4. High-temperature tolerance
5. Flexible

We may share specifics of particular interests once the collaborative research topic has been agreed to.

The efficient transfer of data through high temperature, high radiation, remote, and/or other harsh environments is critical to ensure sufficient guidance navigation control, and information gathering.

Example areas might include:

1. High temperature, rad-hard transducers
2. High-temperature, rad-hard electronic materials
3. High-temperature, rad-hard insulation materials
4. Undersea communication systems
5. Hypersonic window materials
6. Enabling antennae materials and structures
7. High-temperature materials and coatings
8. Dust- and debris-tolerant electronic and PMAD system components
9. Novel sealing/bonding technologies for extreme environments

We may share specifics once the collaborative research topic has been agreed to.

1. Polymeric Materials Development: Developing more accurate polymer-based models will allow for rapid design across various systems and environments (ex. high temperature), high fidelity failure prediction and prevention. This would advance U.S. manufacturing by decreasing development costs and increasing reliability across many business sectors including electronics, biotechnology, and aerospace applications. Current approaches using microscopic models are difficult to integrate at the system level. We aim to develop a lumped element, plug-and-play non-linear model of polymer materials for COMSOL-type environments. With known material parameters and inputted interface features, this model will predict polymer behavior over time, temperature and stress. The focus areas may include smaller electronics systems to a wide range of temperatures and stress, general polymer-ceramic arrays, bonded interfaces, and potted component models.
2. Alloy Design and Development: Developing novel alloys enables unique material properties coupled with stability under harsh mission conditions. Leveraging computational techniques allows for rapid screening of new and existing compositions and a deeper understanding of potential processing conditions and capabilities.
3. Molecular Dynamics: Understanding how the surface chemistry of a material interacts in a harsh environment or how the structure of the material impacts the mechanical properties is critical to many mission applications. The ability to quickly screen or evaluate novel materials facilitates their rapid development, which would otherwise require extensive and challenging testing to gain significant insight. Pre-screening candidates can help focus experimental work on the most promising options

Better methods would allow:

• Automated attachment
• A large number of optical connections
• A very large number of electrical connections (ex. bump bonding).
• Compatibility with different waveguide material systems (Silicon, III-V, LiNbO3, garnet)
• Vertical optical coupling to free space (for LiDAR or
• Free Space Optical Communication applications)
• The ability to couple to different mode shapes and sizes
• The ability to add and remove disposable PICs in the field

It is unlikely that a single method can simultaneously serve all these needs, but they do impact systems in which we have an interest. We prefer, therefore, to meet all these needs with as few unique methods as possible. Many institutions are actively researching improved optical coupling methods to meet the needs shown in Figure 1.

Methods can be divided into two categories, depending on whether they couple light from the edge of the chip or from above. The most common method for coupling light from above is by using a grating. This method lends itself to standard planer fabrication methods but is limited in efficiency and bandwidth. A technique with better performance in these areas is shown in Figure 2. This method uses a 3D mirror to both direct and focus the light above. The fabrication of such a mirror is challenging and we are exploring an alternative method to fabricate integrated mirrors. This method uses the self-assembly of gold nanoparticles, functionalized with DNA, to create pyramids aligned to wells in the substrate, as shown in Figure 3.

This method of light coupling is of interest, as well as other methods, such as adiabatic tapers, photonic wire bonding . Also of interest are the aspects of packaging other than light coupling, such as the physical packaging itself and improved methods for alignment.

While improvements to processes and materials offer the possibility of incremental improvement in the radiation hardness of semiconductor devices, demonstration of vacuum microelectronic systems would represent a step-change in hardness. Our experts would like to pursue a demonstration of this technology and associated processes, such as:

1. Proof-of-concept of vacuum-insulated transistor-like devices at useful voltages.
2. Development of wafer-level packaging techniques compatible with high or ultra-high vacuum inside the package.

Many commercial technologies and processes either have not been evaluated for our mission space or were unable to meet the needs of some of our applications. Characterization of these existing devices or development of novel processes to address these challenges would be extremely interesting. Some example research areas are:

1. Evaluation of long-life, high-reliability uses of commercial devices, such as long-term drift in consumer inertial sensors or stress relaxation in wafer-scale bonds.
2. Heterogenous integration of different processes or technologies, with a focus on leveraging different rad-hard technologies into a single device with multiple capabilities.
3. Development and characterization of wafer-scale bonding techniques that are compatible with the long-term operation of stress-sensitive MEMS components.
4. New concepts for MEMS sensor auto-calibration.
5. Study of anelastic and inelastic behavior in MEMs materials, including dielectric and metal layers.

New processes and technology nodes, such as the 12LP process from GlobalFoundries or gate all around (GAA) field effect transistors, show promise for producing radiation-hardened devices. Systems that require rad-hard technologies typically also require high-reliability performance. These new processes need to be characterized for both reliability and radiation hardness in a variety of environments. Similarly, existing high-reliability processes (e.g., automotive devices) should be evaluated for radiation hardness in the natural space environment (NSE).

Example areas might include:

1. Experimental characterization of devices made on various SOTA technology nodes.
2. Evaluation of neutron single event effects (nSEE) for different nodes and processes across multiple neutron source spectra.
3. NSE evaluation of high-reliability components and sensors. For example, automotive parts for use in space.

Certain components required in a system are more sensitive to radiation than others. These devices are critical to the operation and often require significant compromises in system design to accommodate them. The development of novel approaches that bring about transformative changes in the state-of-the-art would enable a variety of new applications and approaches to address our key challenges. Critical devices we are interested in include power conversion technology, non-volatile memory, and precision voltage and timing references.

We seek advanced concepts, designs, and demonstrations of novel, low SWAP sensing across a wide range of sensing modalities - emphasizing, but not limited to inertial, clocks, optical, CBRN, EO/IR, quantum (sensing only), and magnetic. We are also interested in sensor packaging for harsh environments and sensor integration for broader system use.

Our team seeks advanced concepts, designs, and demonstrations of novel, low-SWAP communications technology and networks. Of particular interest are temporary and dynamic systems in operational environments. Additionally, low-frequency electromagnetic systems (sensors, transmitters, and signal processing) are of merit.

Novel methods for sensor fusion that increase the information content using multiple sensors with different sensing modalities continue to be a challenge we are interested in. Demonstrations of advanced estimation and fusion algorithms with simulated and/or actual data are most beneficial. AI/ML methods should work with sparse data or have application areas known to have abundant data.

We are investing in advancing our mission-planning technology – which has heritage in low-earth orbit (LEO) technology demonstrations, terrestrial undersea applications, and terrestrial unmanned aerial vehicles. We are interested in innovations that advance the state-of-the-art in mission-enabling autonomy to support human and robotic space missions beyond LEO, such as cislunar operations (including the lunar surface), including AI/ML technologies when appropriate, and are certifiable to relevant NASA software standards. Our team may share specifics of particular interests once the collaborative research topic has been agreed upon.

The ability to navigate a spacecraft is critical to all space missions. We are interested in architectures and technologies (both hardware and software) to support autonomous precision navigation in cis-lunar space (including on the lunar surface). Approaches that enable new missions, or fill gaps in current mission concepts, minimize size, weight, and power (SWaP), and can operate autonomously for extended durations in deep space are of particular interest. Again, we may share specifics of particular interests once the collaborative research topic has been agreed upon.

The ability to be situationally aware in space (SSA) requires unique space-based sensors with innovative sensing modalities, mixed sensor suites, and algorithms for the detection, identification, and tracking of objects in both near and far proximity to a space asset (e.g. CubeSat). Our team is interested in novel sensors, algorithms, and system approaches to SSA for LEO through to cislunar domains. We may share specifics of particular interests (e.g. field of view; field of regard; accuracy; etc.) once the collaborative research topic has been agreed upon.

Fault-tolerant computer architectures satisfy NASA safety requirements by providing the reliability and redundancy necessary for human-rated NASA systems, enabled by highly reliable computing elements. These are also required for commercial and science missions that must have mission availability. We are investing in technologies such as our software-based redundancy management (which was developed for NASA’s Space Launch System) and highly reliable electronics to meet the growing need for edge-computing and/or high reliability/fault tolerance in space. We are interested in advances in technologies, algorithms, and approaches to fault-tolerant computing and architectures to support these needs, including radiation hardening and high-temperature electronics. As with the prior research areas, our team may share specifics of particular interests once the collaborative research topic has been agreed upon.

1. Sensitivity to different lighting conditions for vision-based navigation and develop methods to improve VAN robustness to changes in lighting conditions and seasonal terrain.
2. Increased autonomy for TRN onboard vehicles including optimized database search methods, optimized feature detection, and reduced processing power.

1. Explore measurements that do not rely on optical and visual sensors, such as IR and LIDAR measurements.
2. Focus on developing these measurements in shadowed or low-light scenarios, such as at night terrestrially, undersea, in high-contrasting shadows such as at the moon, and at small bodies.

1. Expand our TRN technology to navigate on the surface of Earth or another planetary body such as the Moon and Mars. Focus on wearable tech for humans and rovers.