Neurofuzzy Mission Management System for Multiple Autonomous Vehicles

This paper is concerned with the engineering of Mission Management Knowledge Based processes for the command of multiple Intelligent Autonomous Vehicles. The processing performed in this component of the IAV architecture is concerned with the ordering of robot activities over time and their monitoring during execution. In order for a Knowledge Based Planner to build plans it must have a representation of the knowledge associated with performing actions in the real world. These actions are represented by operators which define the causal relationship between an actions conditions of initiation and the resultant effects of its execution. In this work Neuro-fuzzy causal models have been developed using continuous B-spline representations of fuzzy predicates implemented on an extended popular horn clause logic programming language PROLOG. In addition attempts have been made to support adaption of the causal knowledge base through the modification of clause "degrees of confidence". This has the effect of emphasising certain rules/clauses over others supporting a tuning of the knowledge base to reflect local context dependent domain properties. This extension is significant in that it brings certain Neural Net qualities to the adaption process potentially supporting a more rigorous understanding of adaption and learning in symbolic Knowledge Based systems. Consideration is also made of the use and maintenance of strategic knowledge as a way of improving system performance, both in encouraging increasing intelligent behaviour but also in the improvement of system computational efficiency

Mission Management for Multiple Autonomous Vehicles

This article discusses research into the engineering of Mission Management Knowledge Based processes for the command of Multiple Intelligent Autonomous Vehicles(MIAVs). In particular it discusses architectural and algorithmic considerations in the light of the demanding requirements for robustness and increased system longevity. The architectural issues covered reflect recent developments in Object Technology which has demonstrated the benefits of a componentised view of systems, where observation of interface standards can provide for a 'plug and play' approach to system development and evolution. The algorithmic considerations concentrate on the significant progress in the machine learning field specifically looking at combining popular Knowledge Based Systems(KBSs) approaches with those developed in the area of Artificial Neural Networks (ANNs). Adaptive systems promise resistance to change through the modification of internal models as a result of direct experience of the problem domain, and can, under certain conditions, behave robustly in unseen situations. The engineering of Mission Management Knowledge Based processes for the command of Multiple Intelligent Autonomous Vehicles(MIAVs) concerns the coordination of elements of a distributed system so as to generate coherent behaviour. As such the techniques apply to the management and control of envisaged civil information and automation systems in public utilities, transportation and manufacturing as well as military command and control. The ever increasing demand for cost effectiveness, project efficiency and increasing productivity resulting from open competition is resulting in greater demand for coherent, systems solutions for bespoke large scale projects, such as major building construction, air traffic control and road management systems. These integrated systems are characterised by high capital value and extended life time, which together raise a requirement for evolution in system capability and the acceptance of change as an inherent characteristic of system infrastructure. The acceptance of change implies the need for a rigorous approach to the analysis and design of these systems which emphasises the achievement of modularity. In addition, since these systems often are required to operate in dynamic large, complex, uncertain, unstructured, non-benign environments without human intervention, there is a requirement for an intelligent adaptive ability which can react to environmental dynamics. Adaptive systems are more resistant to system and environmental changes potentially resulting in significant cost saving though increased operational life. The engineering of Mission Management systems for Multiple Intelligent Autonomous Vehicles(MIAVs) in particular and the management problem domain in general places heavy reliance on human decision making and supervision. Computerised management systems have been difficult to introduce primarily as a result of inadequacies in the technology. This is, in part, due to difficulties with describing models of the domain with sufficient precision. Experts in management have a good 'feel' for problems in the domain but, despite being effective managers, find it difficult to express their knowledge in anything but an approximate, vague, rule of thumb way. This conflicts with computer systems requirements which need a precise and complete description of domain relationships. Robotic computerised management solutions traditionally involve the Knowledge Based Planning(KBP) of activities over time and their monitoring during execution. This research extends KBP to approximate rule based systems to support initialisation from expert knowledge, while supporting adaption to fine tune approximate rule sets to better describe domain relationships. This approach takes advantage of expressible human expertise while compensating for inaccuracy, ignorance and incompleteness by supporting adaption, giving systems increased resistance to change and therefore greater longevity. Advantage is taken of recent developments in the use of approximate rule based models in the initialisation of adaptive control algorithms, specifically Neurofuzzy algorithms which can tune an approximate model to reflect arbitrary process relationships. Neurofuzzy Networks not only have the well understood adaptive advantages of Associative Memory Networks but also can be initialised with symbolic fuzzy linguistic rules improving their transparency, and thus aiding in system development and maintainability. This work has, in part, been based on research undertaken in the Advanced Systems Research Group (now the Image, Speech and Intelligent Systems Research Group) on an ESPRIT II project PANORAMA (Perception And Navigation fOR Autonomous Mobile Applications) which ended October 1993. The principle integration testbed was a Mercedes 4-wheel-drive vehicle REMI, additional smaller laboratory based robots were used for local system integration and testing, whilst the developed final demonstration was performed on a tracked drilling machine, owned by TAMROCK (Finland). The drilling machine was required to accurately navigate through an unstructured environment, the intention being to perform drilling operations at various drill sites with a location accuracy of +/-5cm. The 10MECU EU Esprit II CIM project represented a major component in the EU research strategy to address automation problems in its industrial base. This chapter describes a project called PSYCHE which involves the ongoing implementation of a Task Level Mission Management system for cooperating Intelligent Autonomous Vehicles (IAV's) (funded by the ESPRC)

Fuzzy Logic for Task Level Mission Management

An approach adopted for engineering mission management systems for autonomous robots is described. Emphasis is placed on the methods used in the software implementation of the systems and important representations used to support the mission management problem domain. A software prototyping approach based on an extended Prolog written in C++ is discussed. Temporal and fuzzy representations are highlighted as important and necessary to mission management

Task Level Mission Management Systems

An approach adopted for engineering mission management systems for autonomous robots is described. Emphasis is placed on the methods used in the software implementation of the systems and important representations used to support the mission management problem domain. A software prototyping approach based on an extended Prolog written in C++ is discussed. Temporal and fuzzy representations are highlighted as important and necessary to mission management

Fuzzy and Temporal Representations for Task Level Mission Management of IAV's Using Object Orientation.

Task Level Mission Management forms an integral part of the command and control system for truly Intelligent Autonomous Vehicles (IAVs). It is responsible for the overall coordination of robot subsystem activities, directing them to achieve some set of mission goals. The Task Level Mission Management processes for an autonomous land vehicle have been implemented in C++ taking advantage of object oriented techniques. The representations and structure of the software are described and the temporal and fuzzy extensions to the implemented classical planning component are discussed

Implementing Task Level Mission Management for Intelligent Autonomous Systems

Mission Management forms an integral part of the command and control system for truly intelligent autonomous vehicles. It is responsible for overall coordination of robot subsystem activities, directing them to achieve some optimal set of goals. The Mission Management system for an autonomous land vehicle is implemented in C++ using clausal form logic and temporal reasoning. The representations used and the structure are described, emphasising the separation in software of the planning representations from their implementation in C++ classes

The 'early' postprandial glucagon response to a mixed meal is dependent on the rate of gastric emptying in type 2 diabetes

Oral Presentations - OP 28 Desirable diets- Abstract #167Background and aims: Gastric emptying (GE), which exhibits a substantial inter-individual variation, is a major determinant of postprandial glycaemic and insulinaemic responses in both health and type 2 diabetes (T2D). T2D is characterised by attenuated suppression of plasma glucagon after meals, which contributes to postprandial hyperglycaemia. The relationship between the postprandial glucagon response and GE has not been reported. We examined the relationship between plasma glucagon and GE of a standardised mixed meal in well-controlled T2D. Materials and methods: 94 patients with T2D managed by diet and/or metformin monotherapy (61 male, age 64.6 ± 0.7 years, BMI 29.8 ± 0.5 kg/m2 , HbA1c 6.6 ± 0.1% and duration of known diabetes 5.3 ± 0.5 years) were evaluated on a single study day. After an overnight fast, participants consumed a mashed potato meal (1541.8 kJ: 61.4g carbohydrate, 7.4g protein and 8.9g fat, labelled with 100 μL 13C-octanoic acid) between 0-5 min. Venous blood was sampled at t = 0, 15, 30, 60, 90, 120, 180, 240 min for measurements of blood glucose (glucometer) and plasma glucagon (radioimmunoassay). Gastric emptying was assessed by breath test. Data are mean values ± SEM. P < 0.05 was considered statistically significant. Results: After the meal, blood glucose concentrations increased progressively from 8.2 ± 0.1 mmol/L to the peak of 14.0 ± 0.31 mmol/L at t = 90 min, followed by a decline towards baseline. Plasma glucagon increased from a fasting level of 76.1 ± 2.1 pg/ml to a peak of 92.7 ± 2.6 pg/ml at t = 30 min and then decreased to a nadir of 65.6 ± 1.9 pg/ml at t = 180 min. The gastric half-emptying time (T50) was 68.2 ± 1.4 min (range 39-116 min). The incremental area under the plasma glucagon curve between t = 0-30min (glucagon iAUC0-30min) was inversely related to the T50 (r = - 0.3, P = 0.007). The magnitude of increases in blood glucose from baseline at t = 30 (r = -0.3, P = 0.0003), 60 (r = -0.5, P < 0.0001) and 90 min (r = -0.3, P = 0.004) were related inversely to the T50. The increase in blood glucose at t = 30 min was related directly to the glucagon iAUC0-30min (r = 0.3, P = 0.008). Conclusion: In well-controlled T2D, the early postprandial glucagon response to a mixed meal is related to the rate of gastric emptying, and predictive of the initial glycaemic response. These observations support the concept of slowing of gastric emptying to minimise postprandial glycaemic excursions in T2D.W. Huang, C. Xie, N.J.W. Albrechtsen, K.L. Jones, M. Horowitz, C.K. Rayner, T. W