On-board Science Understanding: NASA Ames' Efforts
In the near future NASA intends to explore various regions of our solar system using robotic devices such as rovers, spacecraft, airplanes, and/or balloons. Such platforms will likely carry imaging devices, and a variety of analytical instruments intended to evaluate the chemical and mineralogical nature of the environment(s) that they encounter. Historically, mission operations have involved: (1) return of scientific data from the craft; (2) evaluation of the data by space scientists; (3) recommendations of the scientists regarding future mission activity; (4) commands for achieving these activities being transmitted to the craft; and (5) the activity being undertaken. This cycle is then repeated for the duration of the mission with command opportunities once or perhaps twice per day. In a rapidly changing environment, such as might be encountered by a rover traversing hundreds of meters a day or a spacecraft encountering an asteroid, this historical cycle is not amenable to rapid long range traverses, discovery of novelty, or rapid response to any unexpected situations. In addition to real-time response issues, the nature of imaging and/or spectroscopic devices are such that tremendous data volumes can be acquired, for example during a traverse. However, such data volumes can rapidly exceed on-board memory capabilities prior to the ability to transmit it to Earth. Additionally, the necessary communication band-widths are restrictive enough so that only a small portion of these data can actually be returned to Earth. Such scenarios clearly require the enabling of some crucial decisions to be made on-board by these robotic explorers. These decisions transcend the electromechanical control, health, and navigation issues associated with robotic operations. Instead they focus upon a long term goal of automating scientific discovery based upon data returned by sensors of the robot craft. Such an approach would eventually enable it to understand what is interesting because the data deviates from expectations generated by current theories/models of planetary processes that could have resulted in the observed data. Such interesting data and/or conclusions can then be selectively transmitted to Earth thus reducing memory and communications demands.