Draper Laboratory

External navigational aiding sources such as satellite navigation systems or other static beacons with local coverage are usually required to correct drifts and errors arising from standalone inertial and vision-based navigation. Cooperative navigation enables external aiding among mobile vehicles that share location fixes and reduce each others' localization uncertainty. Each vehicle in a cooperative team may have its own independent objective aside from the joint objective of reducing the team's localization uncertainty. We consider a formulation where areas in the environment with varying levels of navigational aiding are represented by a spatiotemporally varying scalar field, which we refer to as the benefit field. Static (immobile) navigation aids such as beacons can be represented by a time-invariant benefit field. Mobile aids such as one or more cooperative navigational aiding vehicle, can be represented by time-varying benefit fields. The novelty of this work is in the application of optimal control techniques to cooperative navigation. We study optimal trajectory generation to maximize exposure to navigational aiding, thereby reducing localization uncertainty. To this end, we study solutions of the boundary value problem resulting from variational necessary conditions, as well as direct trajectory optimization using the Legendre pseudospectral method. Through numerical simulations, we demonstrate the effectiveness of the proposed methods in reducing the localization uncertainty of the agent.

Natural selection has produced diverse vision systems, from simple patches of photoreceptors to complex camera eyes, representing just one set of evolutionary outcomes. Computational evolution offers a way to systematically test hypotheses, isolate individual factors, and ask the “why” questions behind vision. We recreate vision evolution by coevolving eyes and behaviors in embodied agents and use this to illuminate principles shaping vision across different levels of the Marr’s hierarchy. This leads to three key findings: First, we provide computational evidence that task-specific selection drives bifurcation in eye evolution. Second, we reveal how optical innovations naturally emerge to resolve fundamental trade-offs between light collection and spatial precision. Third, we uncover scaling laws between visual acuity and neural processing that provide insights into long-standing hypothesis behind eye and brain size. Our work introduces a paradigm that uses embodied artificial intelligence (AI) as hypothesis-testing machines that can help accelerate discoveries in vision science.

This paper presents an optimal control solution for an aerocapture vehicle with two control inputs, bank angle and angle of attack, referred to as augmented bank angle modulation (ABAM). We derive the optimal control profiles using Pontryagin’s Minimum Principle, validate the result numerically using the Gauss pseudospectral method (implemented in GPOPS), and introduce a novel guidance algorithm, ABAMGuid, for in-flight decision making. High-fidelity Monte Carlo simulations of a Uranus aerocapture mission demonstrate that ABAMGuid can greatly improve capture success rates and reduce the propellant needed for orbital correction following the atmospheric pass.

We present our recent progress of developing the new CSAC physics package design. Through using right-angle solder joint connections, the wafer scale assembled cores can be integrated into the polyimide suspension in a vacuum package. This approach is the key innovation to achieve low-cost volume manufacturing. We will present the novelties we have done in the core components, including advanced wafer-scale manufacturing of the vapor cells, utilization of metasurface optics to achieve both beam shaping and polarization conversion, and utilization of narrow linewidth VCSELs based on an extended laser cavity. We will also present the clock performance based on a custom-made microwave synthesizer application-specific integrated circuit, which contributes to the power consumption reduction as well as the reduction of required board size.

Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected or is detected too late, worsening pregnancy complications. The gold standard for diagnosing anaemia is a clinical laboratory blood haemoglobin (Hgb) or haematocrit (Hct) test involving a venous blood draw. However, this approach presents several challenges in resource-limited settings regarding accessibility and feasibility. Although non-invasive blood Hgb testing technologies are gaining attention, they remain limited in availability, affordability and practicality. This study aims to develop and validate a mobile health (mHealth) machine learning model to reliably predict blood Hgb and Hct levels in Black African pregnant women using smartphone photos of the conjunctiva. This is a single-centre, cross-sectional and observational study, leveraging existing antenatal care services for pregnant women aged 15 to 49 years in Kenya. The study involves collecting smartphone photos of the conjunctiva alongside conventional blood Hgb tests. Relevant clinical data related to each participant's anaemia status will also be collected. The photo acquisition protocol will incorporate diverse scenarios to reflect real-world variability. A clinical training dataset will be used to refine a machine learning model designed to predict blood Hgb and Hct levels from smartphone images of the conjunctiva. Using a separate testing dataset, comprehensive analyses will assess its performance by comparing predicted blood Hgb and Hct levels with clinical laboratory and/or finger-prick readings. This study is approved by the Moi University Institutional Research and Ethics Committee (Reference: IREC/585/2023 and Approval Number: 004514), Kenya's National Commission for Science, Technology, and Innovation (NACOSTI Reference: 491921) and Purdue University's Institutional Review Board (Protocol Number: IRB-2023-1235). Participants will include emancipated or mature minors. In Kenya, pregnant women aged 15 to 18 years are recognised as emancipated or mature minors, allowing them to provide informed consent independently. The study poses minimal risk to participants. Findings and results will be disseminated through submissions to peer-reviewed journals and presentations at the participating institutions, including Moi Teaching and Referral Hospital and Kenya's Ministry of Health. On completion of data collection and modelling, this study will demonstrate how machine learning-driven mHealth technologies can reduce reliance on clinical laboratories and complex equipment, offering accessible and scalable solutions for resource-limited and at-home settings.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

There is increasing interest in the adoption of distributed, multidomain space situational awareness (SSA) sensor architectures as a means of improving both mission performance and resilience while decreasing total cost. One promising concept calls for augmenting legacy terrestrial architectures with opportunistic sensors hosted on government and commercial spacecraft. This research explores this concept by simulating the observability of resident space objects (RSOs) in medium Earth orbit (MEO) and geosynchronous orbit (GEO) by 3300 candidate sensor network architectures. Over a simulated 48 h period, a network composed of 4 terrestrial telescopes and 10 hosted payloads in low Earth orbit revisited each of over 1200 MEO and GEO RSOs in under 8.5 h. This network also proved to be highly resilient, maintaining this revisit rate in an environment in which all terrestrial telescopes were inoperable. In this maximally degraded environment, over 600 space-based sensor networks were still capable of revisiting each RSO in under 24 h. Results of this research suggest that space-based opportunistic hosted payloads stand to concurrently improve the performance and resilience of existing SSA sensor networks used for deep space object catalog maintenance.