Mission Management for Multiple Autonomous Vehicles

Rayner, N.J.W. and Harris, C.J. (1995) Mission Management for Multiple Autonomous Vehicles

Abstract

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).

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