David Preston Barnes

Landmark recognition for localisation and navigation of aerial vehicles

Work has been undertaken at the University of Wales, Aberystwyth in the area of localisation and navigation of aerial vehicles (aerobots) in large unstructured environments (i.e. natural outdoors). The localisation and navigation method presented in this paper was developed for planetary exploration with an emphasis on Mars, but could also be used on Earth. Mars has an atmosphere, which is dense enough to allow the use of aerobots, and the Mars orbiter laser altimeter (MOLA) has provided the low-resolution topographical map of the surface. The MOLA data has provided the scenery for flight-gear an open source flight simulator, which provided the environment within which all the localisation and navigation experiments have been conducted. Localisation and navigation has been achieved by extracting naturally occurring surface features (landmarks i.e. peaks, ridges, channels etc.) from the topographical maps. By categorising the surface by its features, then by matching these features in a high-resolution topographical map generated onboard the aerobot, with the same features in the low-resolution global map, (e.g. MOLA data) a position estimate is obtained. Once the aerobot has localised, navigation to desired positions can be achieved using a combination of a feature path (feature navigation) and inertial navigation methods. This paper presents the results obtained from the localisation and navigation phases, from the point at which an aerobot obtains topographical maps of the surface, analyse them for features, estimates its position and orientation, to the point of navigating to the desired sites of scientific interest.

Automatic diagnosis of task faults in flexible manufacturing systems

The need for fault tolerant mechanisms in flexible manufacturing systems is described and previous work on diagnosis in robotics and other areas is considered. Fundamental difficulties in the analysis of robot cell malfunctions are described and a glossary of terms useful in this area is presented. Limited observational data on the occurrence of faults in assemblies are reported. Finally a proposal for an experimental mechanism for diagnosis within a knowledge rich supervisory system is explored.

Taking Fuzzy-Rough Application to Mars

This paper presents a novel application of fuzzy-rough set-based feature selection (FRFS) for Mars terrain image classification. The work allows the induction of low-dimensionality feature sets from sample descriptions of feature patterns of a much higher dimensionality. In particular, FRFS is applied in conjunction with multi-layer perceptron and K-nearest neighbor based classifiers. Supported with comparative studies, the paper demonstrates that FRFS helps to enhance the effectiveness and efficiency of conventional classification systems, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions.

PRoViScout: a planetary scouting rover demonstrator

Mobile systems exploring Planetary surfaces in future will require more autonomy than today. The EU FP7-SPACE Project ProViScout (2010-2012) establishes the building blocks of such autonomous exploration systems in terms of robotics vision by a decision-based combination of navigation and scientific target selection, and integrates them into a framework ready for and exposed to field demonstration. The PRoViScout on-board system consists of mission management components such as an Executive, a Mars Mission On-Board Planner and Scheduler, a Science Assessment Module, and Navigation & Vision Processing modules. The platform hardware consists of the rover with the sensors and pointing devices. We report on the major building blocks and their functions & interfaces, emphasizing on the computer vision parts such as image acquisition (using a novel zoomed 3D-Time-of-Flight & RGB camera), mapping from 3D-TOF data, panoramic image & stereo reconstruction, hazard and slope maps, visual odometry and the recognition of potential scientifically interesting targets.

Autonomous Science Target Identification and Acquisition (ASTIA) for planetary exploration

We introduce an autonomous planetary exploration software architecture being developed for the purpose of autonomous science target identification and surface sample acquisition. Our motivation is to maximise planetary science data return whilst minimising the need for ground-based human intervention during long duration planetary robotic exploration missions. Our Autonomous Science Target Identification and Acquisition (ASTIA) architecture incorporates a number of key software components which support 2D and 3D image processing; autonomous science target identification based upon science instrument captured data; a robot manipulator control software agent, and an architecture software executive. ASTIA is being developed and tested within our Trans-National Planetary Analogue Terrain Laboratory (PATLab). This provides an analogue Martian terrain, and a rover chassis with onboard manipulator, cameras and computing hardware. Experimentation results with ASTIA and our PATLab rover are presented.

Experiences with a knowledge engineering toolkit: an assessment in industrial robotics

V. Robinson, N. W. Hardy, D. P. Barnes, C. J. Price, M. H. Lee. Experiences with a knowledge engineering toolkit: an assessment in industrial robotics. Knowledge Engineering Review, 2 (1):43-54, 1987.