This Startup Thinks It Can Make Rocket Fuel From Water. General Galactic, cofounded by a former SpaceX engineer, plans to test its water-based propellant this fall. If successful, it could help usher in a new era of space travel. There's been this hand-wave, this assumption, this at the core of our long-term space programs. If we can return astronauts to the moon, we'll find ice there.

Supporting Go/No-Go decisions prior to launch requires assessing real-time telemetry data against redline limits established during the design qualification phase. Family data from ground testing or previous flights is commonly used to detect initiating failure modes and their timing; however, this approach relies heavily on engineering judgment and is more error-prone for new launch vehicles. To address these limitations, we utilize Long-Term Short-Term Memory (LSTM) networks for supervised classification of time-series anomalies. Although, initial training labels derived from simulated anomaly data may be suboptimal due to variations in anomaly strength, anomaly settling times, and other factors. In this work, we propose a novel statistical detector based on the Mahalanobis distance and forward-backward detection fractions to adjust the supervised training labels. We demonstrate our method on digital twin simulations of a ground-stage propulsion system with 20.8 minutes of operation per trial and O(10^8) training timesteps. The statistical data relabeling improved precision and recall of the LSTM classifier by 7% and 22% respectively.

Boeing's Next Starliner Flight Will Only Be Allowed to Carry Cargo After a high-profile malfunction left two astronauts stranded on the International Space Station, NASA is requiring rigorous testing before humans get back on board. The US space agency ended months of speculation about the next flight of Boeing's Starliner spacecraft, confirming that the vehicle will carry only cargo to the International Space Station. NASA and Boeing are now targeting no earlier than April 2026 to fly the uncrewed Starliner-1 mission, the space agency said. Launching by next April will require completion of rigorous test, certification, and mission readiness activities, NASA added in a statement . "NASA and Boeing are continuing to rigorously test the Starliner propulsion system in preparation for two potential flights next year," said Steve Stich, manager of NASA's Commercial Crew Program, in a statement.

Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations.

This research introduces a novel hydrofoil-based propulsion framework for unmanned aquatic robots, inspired by the undulating locomotion observed in select aquatic species. The proposed system incorporates a camber-modulating mechanism to enhance hydrofoil propulsive force generation and eventually efficiency. Through dynamic simulations, we validate the effectiveness of the camber-adjusting hydrofoil compared to a symmetric counterpart. The results demonstrate a significant improvement in horizontal thrust, emphasizing the potential of the cambering approach to enhance propulsive performance. Additionally, a prototype flipper design is presented, featuring individual control of heave and pitch motions, as well as a camber-adjustment mechanism. The integrated system not only provides efficient water-based propulsion but also offers the capacity for generating vertical forces during take-off maneuvers for seaplanes. The design is tailored to harness wave energy, contributing to the exploration of alternative energy resources. This work advances the understanding of bionic oscillatory principles for aquatic robots and provides a foundation for future developments in environmentally safe and agile underwater exploration.

The Space Domain Awareness (SDA) community routinely tracks satellites in orbit by fitting an orbital state to observations made by the Space Surveillance Network (SSN). In order to fit such orbits, an accurate model of the forces that are acting on the satellite is required. Over the past several decades, high-quality, physics-based models have been developed for satellite state estimation and propagation. These models are exceedingly good at estimating and propagating orbital states for non-maneuvering satellites; however, there are several classes of anomalous accelerations that a satellite might experience which are not well-modeled, such as satellites that use low-thrust electric propulsion to modify their orbit. Physics-Informed Neural Networks (PINNs) are a valuable tool for these classes of satellites as they combine physics models with Deep Neural Networks (DNNs), which are highly expressive and versatile function approximators. By combining a physics model with a DNN, the machine learning model need not learn astrodynamics, which results in more efficient and effective utilization of machine learning resources. This paper details the application of PINNs to estimate the orbital state and a continuous, low-amplitude anomalous acceleration profile for satellites. The PINN is trained to learn the unknown acceleration by minimizing the mean square error of observations. We evaluate the performance of pure physics models with PINNs in terms of their observation residuals and their propagation accuracy beyond the fit span of the observations. For a two-day simulation of a GEO satellite using an unmodeled acceleration profile on the order of $10^{-8} \text{ km/s}^2$, the PINN outperformed the best-fit physics model by orders of magnitude for both observation residuals (123 arcsec vs 1.00 arcsec) as well as propagation accuracy (3860 km vs 164 km after five days).

Kurt Knutsson shows how this companion bot can act like a home security guard and life alert if you have fallen and can't get help on your own. If you are looking for some weird and, in some cases, bizarre tech that will blow your mind, you have come to the right place. We've compiled some of the most fascinating and futuristic gadgets that have wowed us over the past year. From a hamster ball robot that can fly and crawl, to a pair of jeans that can protect you from motorcycle accidents to an AI-powered wearable gadget, these are some of the 10 coolest and craziest things you will ever see. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER The latest sensation in robotics is the Hybrid Mobility Robot (HMR) from Revolute Robotics.

A new drone which flies almost silently without wings or propellers has raised questions about how many supposed UFO sightings might actually be man-made craft. The Silent Ventus drone, made by Florida-based start-up Undefined Technologies, uses ion propulsion, with electrodes ionizing the air to generate thrust, and flies incredibly quietly. The hi-tech drones may possibly explain sightings such as the famous'Tic Tac' drone sighting, where pilots spotted a craft resembling the breath mint performing impossible maneuvers during a training mission with the USS Nimitz off the Southern California coast in 2004. Undefined Technologies' claim that its ion-propelled eVTOL drone generates 150% more thrust than rivals (Undefined Technologies) The company hopes to achieve a 15-minute flight this year and believes the drone could be used for'last mile' deliveries (Undefined Technologies) Undefined Technologies' claim that its ion-propelled eVTOL drone generates 150 percent more thrust than rivals. The company hopes to demonstrate a 15-minute flight with noise levels below 70dB this year - ion drives are widely used in satellites and spacecraft, but less common on Earth.

Last month, the entire world was abuzz when five über wealthy explorers perished at the bottom of the Atlantic Ocean near the grave of the once "unsinkable ship." Disturbingly, during the same week, hundreds of war-torn refugees drowned in the Mediterranean with little news of their plight. The irony of machine versus nature illustrates how tiny humans are in the universe, and that every soul rich or poor is precious. It is with this attitude that many roboticists have been tackling some of the hardest problems in the galaxy from space exploration to desert mining to oceanography to search & rescue. Following the news of the implosion of the Titan submersible, I reached out to Professor F. Javier Diez of Rutgers University for his comment on the rescue mission and the role of robots.

Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computational power than design from scratch and way more flexible than usual scaling approach.