The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon color, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone-camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colors to test this algorithm. The algorithm robustly recognized previously-observed units by their color, while requiring only a single image or a few images to learn colors as familiar, demonstrating its fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International Journal of Astrobiolog

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Machine learning at the interface of structural health monitoring and non-destructive evaluation

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’

Hierarchical self organizing map and focusing inspection strategy for mobile robot novelty detection

Novelty detection is a process of recognizing changes based on learned knowledge. In this research, a novelty detection system was implemented on a mobile robot with an array of sonar sensors for surveillance application. In order to perform novelty detection, a map that stores normal information with respect to any particular robot pose in an environment is required. The map is needed to detect changes and determine the position of novel event. The challenges of mobile novelty detection system are that the false positive rate is usually high whereas the true positive rate is usually low due to mapping and monitoring problems. During mapping, errors due to robot localization and sensor measurement can reduce the quality of the map built. However, available methods in mapping assume perfect localization, hence error in localization is not taken into account in the process of mapping. During monitoring, inspection interval that is too small will consume a lot of time and energy but if the interval is too big, novelty could be missed, hence lower the true positive detection. On top of that, low true positive detection is also caused by the low reliability of sonar sensor measurement. Thus, the objective of this thesis is to utilize mobile novelty detection system by developing a mapping and monitoring strategy that has low false positive detection, high true positive detection and able to estimate the position of a novelty. This thesis proposed two methods regarding to mapping and monitoring process; a hierarchical Self Organizing Map (SOM) and a Focusing Inspection Strategy (FIS). Unlike other mapping methods, hierarchical SOM also consider localization error when associating the normal information with respect to the robot pose. FIS is a multi resolution monitoring strategy which works by changing the frequency of measurement depending on the detection of anomaly. In this thesis, two models were considered; a step (FS) and linear (FL) resolution models. The hierarchical SOM was validated by using simulation and experimentation of the inspection in environment with normal and novel event. False positive rate is measured to determine the map performance. The results show that hierarchical SOM is able to map the normal condition of the environment very well. The inspection results show the false positive rate occurred less than 0.1 at the higher sensitivity setting of 0.9 in either normal or novel condition. The performance of FIS was investigated by using experimentation of the inspection of novel objects of different sizes. The results show that by changing the frequency of measurement using the FS and FL models, the number of true positive detection increases up to 80% when compared to inspection with fix measurement frequency. FIS also reduced the error of position estimation by about 8.8% and 10.9% each for FS and FL and maintained the false positive rate lower than 0.1

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein