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 information, and, finally, testing these hypotheses in some systematic fashion. (See appendix 3B for a hypothetical illustration of this point.)

Lander site selection. During this phase some form of mobile surface device compatible with local environmental conditions is deployed according to planetary orbiter directives. This device performs in situ surface and geologic data acquisition, imaging, and representative physical sample collections. Its deployment requires the selection of appropriate landing sites, a major task for the autonomous exploration system controller.

Processed image data of planetary surface conditions permits a mapping of topographic surface characteristics with respect to terrain configuration ? a cataloguing of mountains, craters, canyons, seas, rivers, and other features to be correlated with maps of temperature, moisture, cloud cover, and related observables. These maps become the basis for a determination of optimal landing locations. Site selection analysis also must include some judgments regarding areas of greatest "interest" for investigation, necessitating some means of detecting regions of the environment which are anomalous with respect to expectations based on prior preliminary analyses of the locale. Criteria for site selection, as for example geological significance or the possibility of lifeforms, are stored in memory. Imagery to be compared to this set of criteria could be obtained from a world model (see chapter 2) developed during the orbital phase, an application ripe for machine intelligence technology development.

Hazard avoidance at the landing site and terrain traversi- bility for mobile landers are additional considerations in the site-selection process. Some mechanism for self-preservation should be included so that an assessment of potential landing sites is made according to whether they pose a danger or are benign. Only then can adaptive action patterns be undertaken with some reasonable expectation of success.

Descent to surface. The descent to surface should be fully automated even in relatively near-future explorations of the Solar System. Autonomous feature-guided landing poses a unique challenge to image-processing technology. For instance, during a parachute descent the target landing site must be located and tracked by an image processor. As the assigned target is tracked, the lander parachute must be manipulated to steer toward the target much like a sports parachute. While the tracking task is not conceptually difficult, the processing speeds required do not exist in present-day computer hardware. As the surface draws closer, the potential landing site must be reexamined for obstacles hazardous to the craft. This presupposes some stored knowledge of precisely what could pose a hazard, as well as the ability to act upon that information. In the Descent phase, machine intelligence integral to the surface exploration system will require high-accuracy processing and ultra-high speed hardware.

On the surface. Once surface contact is achieved the most interesting and probably the most difficult image processing begins. Self-inspection for damage comes first, followed by verification of the lander's position. This may involve comparing the surrounding scene with possible projected scenes assembled from the world model, or the analysis could be based on tracking by the main orbiting spacecraft. Next is the planning, scheduling, and commencement of experiments. All conflicts must be comprehended and resolved. If one experiment calls for rock density measurements and no rocks are within reach of the lander's end-effectors, a decision must be made to schedule another experiment or to move the lander. Such operational decisions require intelligent scene analysis and concept/theory matching.

If preliminary analyses suggest that further investigation is warranted and safe, the lander system for image processing of the surrounding area is deployed. This accompanies the collection of local temperatures, pressures, and general ambient conditions data, as well as sample collection and analysis. To provide these functions the lander (an intelligent robotic device) is equipped with a wide variety of sensor and end-effector apparatus. Vision is especially important for obstacle avoidance and mobility. Stereo vision may prove an invaluable aid in successfully traversing three- dimensional spaces, and also an important safety feature for avoiding depth hazards.

Mobile lander data collection responsibilities require several specific machine intelligence capabilities including (1) pattern recognition to correlate visual images and to detect similarities and differences among data alternatives and (2) decisionmaking to determine whether a particular datum is worth collecting. While it is conceivable that minimal extensions of state-of-the-art expert systems might prove adequate to address the problem of datum "worth," still there remains a sizable gap between current capabilities in computer perception (pattern recognition) and capabilities needed for tasks integral to the proposed mission ? another crucial technology driver.

While some of the Titan mission performance demands on robot manipulators are not as critical as on industrial assembly lines, still there are definite constraints. Spacecraft effectors must operate in completely unstructured environments unlike state-of-the-art factory robots which move only in small, comparatively well-defined work areas. Precision requirements are fairly modest for explorer manipulators when they are handling physical samples, but placement accuracy must be considerably improved whenever the system is responsible for joining closely fabricated pieces during instrument repair, component reconfiguration or construction. Manipulator supervision is supported primarily by visual sensing, though a wide variety of other sensor inputs may supplement optical techniques.

A potentially difficult image processing task is the coordination of manipulator movements with those of the target