MLNav (Machine Learning Navigation)
Description
Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Detailed example
Path planning recommendations for Mars2020 Rover
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Audit / financial statement impact
Low-speed engagements with terrain features on Mars; part of core mission parameters. Mars Rover cannot harm humans or impact rights.
Controls / human review
ATO: No; PIA: Not published
Data needed
Real terrain data from Mars on ENav simulator