Planetary Robotic Field Geology
Derek Pullan, University of Leicester
The development of robotic systems for planetary exploration can learn much from terrestrial field geology. Many aspects of fieldwork undertaken by humans are taken for granted, especially in terms of vision, mobility, dexterity, experiential learning, and complex informed decision processes. Fundamentally, two aspects are common to both human and robotic fieldwork:
- the ability to identify and classify geological features
- the ability to interpret their relevance within a broader scientific context
On Earth, the search for exploitable resources such as oil, gas, water, minerals, geothermal energy etc., although specific objectives, rely heavily on an initial understanding of the fundamental geology of the region being explored. Prior to any field campaign it is important to accumulate all pre-existing data in order to establish local and regional context. This is usually achieved via survey data including geological maps, satellite/aerial remote sensing, geophysical surveys and analysis of samples collected on previous expeditions.
On other planets (including Mars), orbital data from previous missions are likely to be the only source of contextual information prior to landing although some ground truth (albeit inferred) may be available. Surface missions tend to visit new sites and therefore have to undertake basic site investigation in situ with whatever payload assets are available. Although inevitably limited, payloads should include the basic capabilities a human field geologist would consider essential, namely remote to close-up imaging, field analysers to determine rock/soil composition and tools to physically interact with surface materials. In terms of initial reconnaissance, imaging is the most important asset (i.e. PRoViScout). Once the landing site has been characterised, human scientists and/or autonomous systems can then place detailed observations into appropriate context and subsequently make revisions as the mission evolves.
Geological features often appear complex and are influenced by a huge number of variables. In the field, human geologists mentally deconstruct what they see and draw on broader contextual input (the bigger picture) to help classify geological materials and the processes that act on them. Visual observations made in the field, aided by effective use of simple tools such as a hammer and a hand lens, provide an initial vision-based assessment of the local geology. Assessment relies on iteration since features seen from afar often look very different when viewed close up (sometimes unexpectedly so). This emphasises the importance of detailed close-up observations and measurements (payloads must be equipped with appropriate deployable instruments and tools for in situ work), and the need to incorporate re-evaluation in the scientific assessment process.
Vision-Based Geological Reconnaissance - PRoViScout
Geological features and associated parameters fall into three basic categories namely structure, texture and composition. PRoViScout is a mobile reconnaissance platform that will be capable of imaging geological features exhibiting these attributes from a few metres to several hundred metres away. Knowing the distance to the target being imaged, and determining the size of it is important for scientific assessment. Although examples of structure (i.e., layering), large-scale bedding features seen from afar and finely laminated materials seen close-up may represent very different geological processes. Perspective is also important, particularly at near-field to remote distances where stereo enhances structural interpretation and at close-up distances where it reveals texture or surface relief. Determining the composition of targets using multispectral imaging also depends on distance and the scale of features exhibiting that particular attribute.
In addition to ground-based imaging, PRoViScout also proposes to incorporate aerial imaging using a tethered aerobot to aid navigation and traverse planning. Imaging from this different perspective will also complement the scientific information acquired by the surface platform (rover). For science target assessment the aspects of distance, perspective and feature scale previously described apply to aerial data also.
For PRoViScout science assessment will be based on the identification and classification of a limited yet representative selection of fundamental attributes (parameters) associated with geological features ranging from types of layering to grain morphology to hue. Some parameters are scale-specific (i.e., grain size) and others applicable at all scales (i.e., layering geometry). Images of cartoons [Figure 1], synthetic targets [Figure 2], real field specimens [Figure 3], terrestrial field sites [Figure 4], and extraterrestrial field sites [Figure 5] illustrating a range of attributes will be used for testing the target identification software.
Figure 4: Layered turbidite deposits at Clarach Bay, Aberystwyth, UK.
Image credit: Derek Pullan.
Figure 5: Composite sedimentary structures and
textures at Cape St. Vincent, Victoria Crater, Mars.
Image credit: NASA/JPL.
The attributes and parameters being considered cover a wide range of geological features some of which may not be encountered on Mars. This is intentional since any autonomous robotic system that is expected to undertake serendipitous exploration (including PRoViScout) must be able to cope with unknowns based on fundamental principles (the generic approach) as well as implied knowledge (the analogue approach).
Once identified using a variety of image processing techniques (currently being developed) each attribute will be assigned a scientific ranking or “score” depending on the importance of the feature it represents within the context of a mission scenario. In reality, scores would need to be accumulated for geological features made up of individual attributes i.e., composite features using potentially complex algorithms. As mentioned previously, knowledge of feature scale will influence both classification and ranking. Once targets have been prioritised in terms of “scientific importance” then this information can be used to autonomously influence operations (such as deviate from the planned sequence) and thus start to emulate, albeit basically, the decisions and actions of a human field geologist.
Multi-thematic Geological Reconnaissance – Beyond PRoViScout
As a vision-based system PRoViScout is well-equipped to observe science targets exhibiting structural and textural attributes at a variety of scales in both 2D and 3D. Composition on the other hand has to be inferred from fundamental visual parameters (i.e. colour etc), spectral absorption from a few narrow-band filters across the visible/near-infrared spectrum and fluorescence properties. Definitive determination of composition by direct in situ elemental, mineralogical and/or molecular spectroscopy for example is not part of the PRoViScout remit. However, it is important to note that these types of measurements are essential to corroborate, question or invalidate visual thematic reconnaissance data. Furthermore, access to representative material (i.e., fresh rock, morphological biosignatures etc) may require some sort of geotechnical activity such as grinding to remove any superficial weathering/alteration products that could be compositionally different. Both these types of activity (in situ analysis and geotechnics) provide the final decisive step prior to sample acquisition but are beyond the scope of PRoViScout. They will logically be incorporated into follow-on studies.
© Derek Pullan
© Derek Pullan