Knowledge based Science Target Identification System (KSTIS)

Derek Pullan, Laurence Tyler, Stephen Pugh, David Preston Barnes

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A major mission driver for unmanned space exploration is to maximise science data return whilst minimising ground-based human intervention and hence associated operations costs. Future robotic exploration such as the ESA ExoMars mission (launch 2018), and the subsequent Mars Sample Return (MSR) mission will require rovers to travel further and faster than has been achieved to date. However, despite recent advancements in technology there still exists a conservative attitude towards planetary exploration. This is primarily caused by the limited number of space missions and the substantial cost of these missions. For example, the five extended mission stages of the NASA/JPL MER Rovers cost over $120,000,000; this results in substantial rewards for success and severe penalties for failure. With this in mind it is currently unlikely that a fully functional artificially intelligent autonomous system will be deployed upon an extra terrestrial planetary surface in the near future. Despite this, the recent success of the NASA/JPL MER Rovers has provided an excellent opportunity to experiment with autonomous and automatic modules on Mars. The success of these modules coupled with advancements in long range communications is leading to an increased amount of data being returned to Earth for scientific assessment. KSTIS has been designed in order to alleviate this load and also provide help in attaining assessment consistency.
Original languageEnglish
Publication statusPublished - 29 Aug 2010


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