Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms

Fuzzy Optimisation of Structural Performance

Structural performance has become a fundamental concept in advanced engineering design in construction. Basic criteria concerning the action effects and imposed performance requirements are analysed assuming two types of uncertainties: randomness and vagueness. The natural randomness of the action effect is handled by commonly used methods of the theory of probability. The vagueness of performance requirements due to indistinct or imprecise specification and perception is analysed using the basic tools of the theory of fuzzy sets. Both types of uncertainties are considered to define fuzzy probabilistic measures of structural performance, the damage function and fuzzy probability of failure. Fuzzy probabilistic optimisation of vibration constraints indicates that the limiting values recommended for the acceleration of building structures may be uneconomical. Further research should focus on verifying of the input theoretical models using available experimental data.

Multi-objective optimisation in air-conditioning systems : comfort/discomfort definition by IF sets.

The problem of multi-objective optimisation of air-conditioning (AC) systems is treated in the paper in the framework of intuitionistic fvzzy (lF) set theory. The nature of the problem is multi-objective one with requirements for minimal costs (generally, life cycle costs; more specifically, energy costs) and maximal occupants' comfort (minimal discomforl). Moreover, its definition by conventional means is bounded to a number of restrictions and assumptions, which are often far from the real-life situations. Attempts have been made to formulate and solve this problem by means of the fuzzy optimisation [4]. The present paper makes further step by exploring the innovative concept of IF sets [6] into definition of the trickiest issue: comfort and discomforr definition. The new approach allows to formulate more precisely the problem which compromises energy saving and thermal comfort satisfaction under given constraints. The resulting IF optimisation problem could be solved numerically or, under some assumptions, analytically Il]-t21. An example illustrates the viability of the proposed approach. A solution which significantly (with 3S%) improves comfort is found which is more energetically expensive with only 0.6Yo. This illustrates the possibility to use the approach for trade-off analysis in multi-objective optimisation of AC systems

Enhancing sustainability of chemical plant operations through dual objective holistic optimisation - the case of an integrated ammonia and nitrogen-derivatives production facility

In recent years, there has been much improvement in the theory and application of mathematical optimisation. Optimisation techniques have now been developed for conditions of uncertainty (fuzzy) and probability (stochastic) and together with existing methodologies, such as linear programming and multiple objectivity, a very powerful set of tools is now available to enable the determination of the ‘best’ solution for most operational scenarios under a variety of uncertain operating conditions. Optimisation techniques are currently available for most scenarios involving conditions of uncertainty, e.g. Fuzzy Optimisation, Stochastic Optimisation and Multi-Objective Optimisation. However, very few techniques exist for combinatorial optimisation scenarios, e.g. Stochastic Fuzzy Optimisation and Multi-Objective Fuzzy Optimisation and only one optimisation technique was discovered that covered three different conditions of uncertainty, i.e. Multisub- objective Stochastic Fuzzy Optimisation. However, in the chemical industry, quite a few production operations exist that would greatly benefit if an optimisation methodology existed that covered four different simultaneous conditions of uncertainty, i.e. Multiple Objectivity, Fuzziness, Stochastics and Minmax (simultaneous maximum and minimum solution). A case in point is the interrelated production of ammonia (NH3) and its downstream nitrogen-derivatives such as nitric acid (HNO3), ammonium nitrate solution (NH4NO3.H2O), ammonium nitrate (NH4NO3) and limestone ammonium nitrate. Such an operation is characterised by conditions of Fuzziness (uncertainty in product demand), Stochastics (probability distribution of hydrogen in coal, one of the ammonia production raw materials), Multi-objectives (e.g. the need to simultaneously maximise production in a number of different plants) and Minmax (e.g. the need to maximise production while simultaneously minimising effluent discharge) In this research project, a 4 – Way (Multi-sub-objective, Stochastic, Fuzzy, and Minmax) Optimisation methodology was successfully derived, based on existing singular optimisation methodologies, and successfully applied to the interrelated ammonia and downstream nitrogen-derivatives production facility. The Holistic Optimisation methodology derived could be easily applied to a wide variety of chemical and operational scenarios

Soft computing applications in dynamic model identification of polymer extrusion process

This paper proposes the application of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy ruled-based system is introduced into the analytical model of the extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing the optimal structure for the sub-models, a hybrid algorithm of genetic algorithm with fuzzy system (GA-Fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions. The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction to the theoretical analysis. Then, the effectiveness of adaptive sub-models in approximating the operational-sensitive parameters during the operation is further investigated

Optimisation problems as decision problems: The case of fuzzy optimisation problems

The importance that decision-making problems and optimisation problems have today in all aspects of life is beyond all doubt. Despite that importance, both problems tend to be thought of as following different routes, when they have, in fact, a “symbiotic” relation. Here, we consider the different decision problems that arise when different kinds of information and framework of behaviour are considered, and we explore the corresponding optimisation problems that can be derived for searching the best possible decision. We explore the case where Fuzzy Mathematical Programming problems are obtained as well as other new ones in the fuzzy context.Research supported by the project TIN2014-55024-P from the Spanish Govern as well as by the project TIC-8001 from the Andalusian Govern (both financed with FEDER funds)

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Earthworks planning for road construction projects: a case study

In this paper we construct earthwork allocation plans for a linear infrastructure road project. Fuel consumption metrics and an innovative block partitioning and modelling approach are applied to reduce costs. 2D and 3D variants of the problem were compared to see what effect, if any, occurs on solution quality. 3D variants were also considered to see what additional complexities and difficulties occur. The numerical investigation shows a significant improvement and a reduction in fuel consumption as theorised. The proposed solutions differ considerably from plans that were constructed for a distance based metric as commonly used in other approaches. Under certain conditions, 3D problem instances can be solved optimally as 2D problems