Demonstration Mission may be appropriate in computing navigational corrections, but subsequent deep-space exploration requires a fully autonomous navigation system. Such systems also improve cost-effectiveness by reducing the amount of ground support necessary to accomplish the missions. Potential savings in equipment complexity, operational costs, and processing time will motivate the development of autonomous systems for near-Earth and deep-space vehicles.
Consider, for instance, the Viking mission, one of the most complex interplanetary operations attempted to date. The Mars landers were remotely operated robot laboratories equipped with comparatively highly automated instrumentation. Many spacecraft functions could be performed adaptively, accommodating to changing necessities during the mission. Even so, the operational system required major navigational changes to be specified 16 days before the indicated flight action. Several hundred people on Earth were involved in science data analysis, mission planning, spacecraft monitoring, data archiving, data distribution, command-sequence generation, and system simulation. An infusion of advanced machine intelligence could significantly reduce this major mission cost.
In addition to navigation, the spacecraft also must maintain attitude and configuration control, thermal control, and communications links. These functions involve the use of feedback loops and built-in test routines. One way to visualize a greatly improved system is to conceptualize a machine intelligence capable of sequentially modifying its activity as a result of experience in the environment, with an additional capability of internalizing or "learning" the relationship between environmental states and corrections to guide future modifications and coordinate them with anticipated states. Such goal-directed intelligent functioning is not possible with state-of-the-art AI technology. However, it is conceivable that a machine system could be provided with a capacity to represent its present state, some goal-state of equilibrium or stability and a means of noting and measuring any discrepancy between the two, and, finally, effectors or actuators for modifying the present state in accordance with the programmed goals.
Search. During the Search phase the system performs preliminary analyses while approaching the target body. The information acquired is integral in making decisions about subsequent activities as well as the point at which to begin preliminary analysis. The spacecraft must be able to employ appropriate sensing equipment to collect raw data and to modify sensor utilization as a result of feedback information. Inherent in this formulation is the capacity of the system to perform some analysis using the raw data it has collected and to make decisions about mission sequencing based on analysis results.
Complementary and concurrent sensing tasks are scheduled according to the time required for their completion, the point at which their output becomes important to ongoing model construction, and the relative importance of the results. Another significant factor is spacecraft- instrumentation power scheduling, assuming that the supply of energy is insufficient to allow all subsystems to operate simultaneously. Scientific tasks must be scheduled to take into account possible mission-control functions that might override them. Collection tasks producing data having multiple uses or particular utility in mission integrity operations (self-maintenance, survival, and optimization) have high priority. All operations are to be accomplished without benefit of direct human intervention.
For the initial Titan mission, one might attempt to automate all search functions by means of an onboard expert system that utilizes known information about the conditions on Titan and that is capable of examining and choosing from among preselected resident hypotheses (leading finally to some judgment as to what action to take based on probability calculations). However, such a system could be highly fallible because information gaps and inaccuracies in its available range of hypotheses might lead to serious mis- judgments. In the case of the long-term objective interstellar navigation the consequences of an incomplete knowledge base are even more dramatic. The team concludes that expert systems of the current AI variety cannot satisfactorily perform the Search task.
One possible solution, and a potentially valuable technology driver, is an advanced type of expert system able to update and modify its own knowledge base as a result of experience that is, as a result of the analytical actions which it performs on its own environment. On Earth the advent of such an advanced system would eliminate time- consuming and costly human analysis and reprogramming typical of state-of-the-art expert systems (which would be particularly inefficient in space applications where huge time delays often must be accommodated). Self- modification of advanced expert systems also prepares the exploration system to make autonomous decisions and corrections regarding its relationship with the environment.
An additional essential task en route to an unknown planetary system around another star is the determination of gross parameters such as sizes, masses, densities, orbital periods, rotational periods, axial tilts, and solar distances for each member planet and moon. A fully autonomous spacecraft would utilize these characteristics, determined by early data collection, in making onboard selections of appropriate bodies to explore.
Given the existence of specific atmospheric conditions determined by long-range remote sensing, logical hypotheses may be generated to predict the surface conditions of the chosen celestial body in terms of the possibility of life and the compatibility of the planet with spacecraft hardware and engineering. Decisions must then be made on the basis of preliminary analyses whether to proceed and establish orbit around the planet for further exploration, or to
