In the early 1960s, during the space race, neither American nor Soviet scientists really knew where planets like Mars or Venus were—especially at the accuracy and precision essential for spacecraft navigation. That may sound faintly ludicrous. They of course knew roughly where a target like Venus would be when a spacecraft got there. But “roughly” in this context might be an offset of 10,000 or 100,000 kilometers. Planetary positions, their ephemerides, rely on the calibration of their orbits to extremely high precision over time. But the only way to do that properly is to make direct measurements, just as the mariners of old would need to sail right by an island or shoreline in order to nail down its latitude and longitude. [...]
It remains to be seen whether tiny spacecraft can carry the requisite computational toolkit or sensory and steering capacity to do this. The bright stars themselves might be the best markers to exploit, together with our own sun forming a navigational beacon. Tiny pulses from miniature laser diodes could provide thrust to maneuver with, and perhaps the key is to send hundreds, even thousands, of nanocraft, each with modest AI and an ability to learn from each other and to reach their goals in space and time through massive redundancy and the sacrifice of many. But when you’re trying to catch a flying bullet—whether star or planet—with another flying bullet, things can go wrong. [...]
In sending machines to other worlds, even to other stars, we have no choice but to fully admit our inaccuracies and imprecisions, to be entirely, brutally honest about our limited grasp of what’s out there. Even the laws of nature are deductions based on wholly imperfect measurements, whether of planetary orbits and gravity, or of the properties of logic and symbolic manipulation in algebra—the latter being “measured” through human minds and the machines those minds produce. The amazing thing is how well these laws let us model and predict aspects of the physical world, a capacity that has reassured and helped us for thousands of years. We have managed to turn the problem around, and can now predict the kinds of chaos that should occur across nature, from unsettled weather conditions and unstable stock markets to, of course, planets.
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