AsterWise uses a network of onboard sensors linked with real-time data processing algorithms to continuously analyze the spatial environment of a probe.

Advanced machine learning models interpret sensor data to anticipate asteroid movements and calculate optimal evasion maneuvers.

The system acts in real-time, autonomously adjusting the probe’s velocity and trajectory to avoid collisions without requiring intervention from ground control.

It leverages the stochastic modeling of asteroid fields, incorporating past data and simulations to enhance predictive accuracy.

The flexible algorithms prioritize computational efficiency to operate within the probe’s limited processing capabilities, thus ensuring seamless integration without overburdening system resources.

AsterWise drastically reduces the risk of collision with dynamically moving asteroids, enhancing mission safety and success.

Its real-time adaptability ensures a more efficient use of resources compared to static navigation systems, and it lowers mission costs by minimizing the risk of damage or failure.

The core technology of AsterWise—real-time data processing and machine learning—is mature, but its application in deep space requires rigorous validation.

The high R&D costs and need for precision and reliability pose technical challenges, though partnerships with space agencies could facilitate development.

The integration of such systems with existing navigational architectures needs extensive testing to meet reliability standards.