Enhanced VML 2.1 allows autonomy to be created for commercial low earth orbiting observation missions that would permit targets of opportunity for observations to be identified and acted upon. Autonomy in this regime could also simplify spacecraft operations by allowing onboard systems to make more decisions, and reduce the need for off-shift operations personnel. Many of the NASA commercial applications listed above also have potential terrestrial applications. VML 2.1 autonomy capabilities could be applied to autonomous vehicle control, manufacturing process controllers, airborne systems, and remote science stations with limited contact time. The state-machine approach has an advantage over autocoded systems in that the embedded software is not unique for every flight software load, reducing risk and enhancing system insight.
RRDS may be applied to a variety of commercial missions reactively operating spacecraft in complex scenarios, like deep space missions retrieving samples from a variety of planetary bodies, comets, asteroids, or moons. RRDS could also be used on uncrewed cargo flights to a space station or assembly site. Executable state machines provided by the enhanced VML 2.1 allow many kinds of autonomy to be created, outside of the RRDS realm. These include: reactive fault protection which is cheaper to develop and more transparent in operation than a flight software implementation autonomy for self-directed orbital missions requiring limited operational interaction with controllers, reducing personnel costs autonomy for self-directed comet / asteroid sampling missions requiring limited operational interaction with controllers, reducing DSN time and personnel costs on-board replanning to compensate for degraded and failed systems in a high radiation, remote environment like Europa orbit autonomy for landed vehicles and rovers, reducing the risk to the mission and simplifying mission operations target-of-opportunity science collection in earth-orbiting or deep space environments, allowing detected events to result in further detailed observations (e.g. detected volcanic activity leading to taking a raster of images) expert systems for guiding remote experiments in real-time based on observed environmental conditions