Speaker
Description
Space missions are continuously increasing in complexity, especially as spacecraft explore unknown terrains and encounter unpredictable situations. This complexity is compounded by the extreme distances some spacecraft operate from Earth, which makes real-time ground control impractical. Higher levels of autonomy and greater on-board navigational precision are needed to solve these issues.
Currently machine learning is an active area of research by automotive and other industries that is being used in conjunction with electro-optical sensing systems for object detection, classification and tracking, enabling cars to build a scene of their surroundings. This enables a wide range of automation levels for these vehicles, from advanced driver assistance systems (ADAS) to fully autonomous driving.
Can machine-learning techniques be similarly applied to space systems to improve the capabilities of the spacecraft, while simultaneously overcoming cost and resource challenges of today’s complicated missions? A fundamental challenge to this approach is the traditional conservatism of the industry, which has historically favored reliability and testability over performance.
This talk will discuss how machine learning is already seeing applications in the industry and explore some methods for overcoming the obstacles, such as verification and validation concerns, preventing its further adoption.
| Paper submission | No |
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