object, better known as "hand-eye coordination." Image processing may be used accurately to find the position (in three-dimensional space) of the object to be manipulated as well as the grasping surfaces of the manipulator itself. Locating these surfaces might involve matching the received images with memorized models (or concepts) of the object and the end-effector, a tremendous challenge to present-day machine intelligence technology. An alternative method requires using pressure- and force-feedback, as well as proprioceptive information (sensory input designating body or effector orientation) to reduce image processing requirements.
Movement of the lander demands that a safe, obstacle- free path be found across the landscape. This may entail generating a contour map of the surface surrounding the lander (perhaps using high-resolution satellite/orbiter data) and derivation of a clear path from this map. State-of-the- art laser scanning techniques already have proven adequate to handle the task of topographic analysis for purposes of local wild-terrain locomotion. Hazards hidden from view along the intended itinerary must be identified en route, and the path ahead continually re-scanned and updated as in the case of a human walking through a rocky area.
An alternative (and more difficult) approach places greater reliance on autonomous lander processing systems. A planet model provides an apparently traversible path from the landing site to another location observable from the landing site (based on low-resolution data). This "fuzzy" trail is given to the lander controller which then must negotiate its own path from the first position to the second, must identify and work its way around such obstacles as gulleys, creeks, or rubble invisible in the low- resolution model. In addition, during each traverse the lander analyzes the surrounding scenery and searches for significant or unusual objects while also keeping track of its location. Thus, a great deal of image processing and map updating must be done that requires formidable onboard computing power, as well as advanced machine intelligence techniques.
Build. The Build phase actually lies in the domains of space manufacturing (chapter 4) and machine replication (chapter 5), but nevertheless, is worth mentioning here as an important prerequisite for extending the proposed mission to intensive Solar System and interstellar exploration. At some (yet undefined) point it becomes necessary to provide machines with mining, materials processing, construction, repair, and perhaps, even replicative capabilities in order to escape the enormous cost of building and launching burgeoning masses of exploration equipment from Earth (Freitas, 1980b). With respect to the Titan Demonstration Mission, a first step toward the ultimate goal of machine self-sufficiency would be an onboard provision for machine hardware components with the ability to make adaptive modifications to the system as a result of preliminary analyses of probe and landing craft needs. 3.2.2 Scientific Investigation: Remote Sensing and Automated Modeling
The concept of space exploration presented above suggests the potential capability of an interstellar spacecraft to develop complete detailed models of planets and moons in other solar systems and to return these to Earth as major scientific discoveries about the Galaxy. These models would include information about the planets' atmospheres, surfaces, subsurfaces, electromagnetic and gravitational fields, and any evidence of lifeforms.
Having first characterized the operational mission stages and identified the important machine intelligence requirements of each, the Space Exploration Team chose to consider at greater length one aspect of the Titan Demonstration system capacity to conduct useful scientific investigations: automated modeling of an unknown celestial body. This particular aspect of the scientific investigation capability was selected because it involves the full range of high-level machine intelligence required for autonomous space exploration, while simultaneously relating to the orbit-based world model deployment scheme contemplated by the Terrestrial Applications Team (see chapter 2).
In terms of the preceding discussion of the operational phases of space exploration missions, the task of creating such models is the first and foremost task of the Orbit stage. Detailed remote sensing is undertaken in the mission orbital phase to complete atmospheric modeling and to map various physical parameters of the surface. Perhaps as much as 90% of the total information gathered in the exploration of an unknown body can be collected by the orbiter.
A complete world model describes atmospheric and surface physical features and characterizes the processes which govern the dynamic states of the planet and its atmosphere. The job of constructing a world model may be broken down into two separate categories: building an atmospheric model and examining processes in the surface environment, described below. Since a great deal of work is under way at NASA and at various universities in the analysis of Landsat and weather satellite information, it can be anticipated that much of the groundwork in the techniques for assembling a planetary model will have been laid long before deployment of the Titan mission. Not only is the development of a terrestrial world model an essential precursor research program in pursuit of interstellar mission technical requirements, but it also provides valuable Earth resource information in the more immediate future. Creating and automatically modifying world models based on inputs from a variety of sensors is a machine intelligence technology in which research should be encouraged.
