With the increasing demand for high-altitude high-speed vehicles in complex adversarial environments, autonomous navigation under conditions of GNSS signal unavailability or damaged ground infrastructure has become a critical technological bottleneck. Existing navigation means—including visual navigation, terrain matching, inertial navigation, celestial navigation, and geomagnetic navigation—each have inherent limitations, making it difficult to simultaneously satisfy requirements for all-weather operation, high accuracy, and long endurance. Inspired by the use of relative position vectors for absolute navigation in spacecraft formation flying, this paper extends this principle to high-altitude high-speed vehicles that are significantly influenced by non-conservative forces such as engine thrust and aerodynamic forces. However, it is necessary to utilize accelerometer measurements from inertial navigation to subtract the effects of non-conservative accelerations, so that the gravitational acceleration information—which reflects the absolute position—can be extracted from the relative position vectors. Nevertheless, the influence of multi-source uncertainties—including accelerometer noise and bias, relative position measurement errors, and star tracker attitude errors—on navigation accuracy remains unclear, and how to achieve high-precision autonomous navigation under this framework remains a key issue to be resolved.

As orbital space resources become increasingly scarce, the problem of orbital pursuit-evasion games among spacecraft has attracted widespread attention. In multi-to-one pursuit-evasion scenarios, multiple pursuit spacecraft are required to cooperatively capture a non-cooperative evasive target under impulsive maneuver and fuel constraints, giving rise to an optimal control problem characterized by strong nonlinearity and complex constraints. Existing differential game methods suffer from high computational complexity and poor real-time performance; numerical optimization approaches are sensitive to initial guesses and struggle to handle long-duration constraints; and although deep reinforcement learning has demonstrated advantages in some scenarios, it lacks proactive prediction capability when confronting unknown evasion strategies, thereby limiting pursuit performance. Consequently, how to predict the actions of the evading spacecraft in multi-to-one scenarios and accordingly optimize the cooperative strategies of multiple pursuers has become a key challenge for improving pursuit efficiency in complex orbital games.

Roughly one-third of the food produced worldwide is wasted, and researchers at Kyushu University are turning to packaging for a solution. Their review brings together intelligent sensing materials, self-healing films, and AI-driven prediction to expand packaging's role from passive protection to active, real-time sensing. Through a closed loop of recognition, judgment, actuation, and feedback, the team aims to make packaging a trusted, responsive part throughout the food system, from producers to consumers.

A new study helps show how builders and city planners can minimize the materials needed to accommodate the planet's future population.