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.

A white-light lidar concept could measure ocean remote-sensing reflectance (R_rs) without relying on sunlight.

As high-value orbital regions become increasingly congested, pursuit–evasion confrontations among spacecraft have emerged as a critical issue in the field of space security. Active defense strategies, in which a target spacecraft releases defensive vehicles to implement counter-interception against the pursuer, constitute a three-body engagement scenario involving the target, the pursuer, and the defender. However, in actual engagements, the presence of observation noise, incomplete information, and multi-strategy adversarial conditions arising from uncertain pursuit policies render existing guidance methods—whether based on unilateral optimal control, differential games, or conventional reinforcement learning—incapable of simultaneously addressing the multiple challenges posed by unknown pursuit strategies, information deficiencies, and high-maneuverability confrontations. Consequently, how to achieve cooperative adaptive guidance between the target and the defender under conditions of incomplete observations and variable pursuit policies has become a key technological bottleneck for enhancing spacecraft survivability.

With the rapid advancement of global aerospace technology, human space exploration is progressively extending from near-Earth orbit to the lunar sphere. Large-scale deep-space exploration activities, such as the construction of lunar research stations and lunar bases, are gradually becoming a reality. Major spacefaring entities, including China, the United States, Europe, and Japan, have successively proposed their own lunar exploration and long-term residency programs. Current lunar exploration missions now encompass complex scenarios such as scientific investigation, in-situ resource utilization, lunar construction, and operational maintenance, which impose entirely new demands on lunar robotic technologies. However, existing lunar and Mars exploration missions still predominantly rely on pre-programmed commands and ground-based teleoperation, exhibiting limited intelligence levels. They face significant challenges in addressing five core issues: multi-task high-precision manipulation, autonomous navigation and obstacle avoidance, self-learning interactive collaboration, adaptation to extreme environments, and high-reliability long-duration operation. Consequently, the systematic integration of emerging technologies—including artificial intelligence, deep learning, large-scale models, and embodied intelligence—into lunar robotic design, so as to achieve the transition from programmed robots to intelligent robots, has become a critical technological bottleneck for supporting future large-scale lunar exploration and development.