AbstractA major mission driver for space exploration is to minimise groundbased human intervention and hence associated operations costs, thereby maximising science data return. Future robotic exploration such as the ESA ExoMars mission will require rovers to be equipped with greater autonomy.
In line with such a requirement, a new autonomous system named Autonomous Rock Science Analysis System (ARSAS) is proposed in this thesis for the purpose of identifying and accessing scienti c rocks during exploration. ARSAS consists of three components: rock detection, rock science value evaluation and related executive agent.
Three approaches are presented in the rock detection component. A number of image processing and machine learning techniques have been employed, including multispectral sampling, fuzzy-rough feature selection, classi cation, clustering, thresholding and saliency methods.
The rock science evaluation component is primed by a human planetary geology expert. Some visual features are selected as the indicators of some geological attributes and then a fuzzy expert system is used to convert the rock attributes to corresponding science value. In contrast with previous works, the proposed science evaluation mechanism is more autonomous and geology-oriented.
The executive agent mainly consists of a pair of cameras and a robotic arm, together with a series of algorithms for coordinate transformation. It serves as a platform to support the previous two components. Experiments have been conducted on this platform to demonstrate the usefulness, stability and repeatability of the proposed system.
The details of design, implementation and experimentation of all components are elaborated in the thesis.
|Date of Award||15 Jan 2016|
|Supervisor||Frédéric Labrosse (Supervisor) & Laurence Tyler (Supervisor)|