Real time evolutionary algorithms in robotic neural control systems.

This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context on-line means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is off-line, as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEAs ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed

Evolutionary algorithms for real-time artificial neural network training.

This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the algorithm is designed to be used with a physical robot, the population size was one and the recombination operator was not used. The algorithm is therefore rather similar to the original Evolutionary Strategies concept. The idea is that such an algorithm could eventually be used to alter the locomotive performance of the robot on different terrain types. Results are presented showing the effect of various algorithm parameters on system performance

A front-end system to support cloud-based manufacturing of customised products

In today’s global market, customized products are amongst an important means to address diverse customer demand and in achieving a unique competitive advantage. Key enablers of this approach are existing product configuration and supporting IT-based manufacturing systems. As a proposed advancement, it considered that the development of a front-end system with a next level of integration to a cloud-based manufacturing infrastructure is able to better support the specification and on-demand manufacture of customized products. In this paper, a new paradigm of Manufacturing-as-a-Service (MaaS) environment is introduced and highlights the current research challenges in the configuration of customizable products. Furthermore, the latest development of the front-end system is reported with a view towards further work in the research

Cloud-based manufacturing-as-a-service environment for customized products

This paper describes the paradigm of cloud-based services which are used to envisage a new generation of configurable manufacturing systems. Unlike previous approaches to mass customization (that simply reprogram individual machines to produce specific shapes) the system reported here is intended to enable the customized production of technologically complex products by dynamically configuring a manufacturing supply chain. In order to realize such a system, the resources (i.e. production capabilities) have to be designed to support collaboration throughout the whole production network, including their adaption to customer-specific production. The flexible service composition as well as the appropriate IT services required for its realization show many analogies with common cloud computing approaches. For this reason, this paper describes the motivation and challenges that are related to cloud-based manufacturing and illustrates emerging technologies supporting this vision byestablishing an appropriate Manufacturing-as-a-Service environment based on manufacturing service descriptions

Industrial challenges in patent management and crowdsourcing patent landscapes for engineering design innovation

Innovation is critical to sustain in prevailing competitive business environments. Industries need effective innovation strategies in-practice to develop and deliver novel products and services swiftly. In order to implement innovation strategies effectively, industries need innovation capacity in engineering design supported with intellectual assets. However, there are many issues that prevent streamlining these processes. The objectives of this research are to explicit the issues related to industrial patents (one of the important resources in intellectual assets) generation and management processes, and propose cost-effective crowdsourcing approach as a tool for patent landscaping activities. Interviews with patent attorneys and intellectual audit specialists reveal that most industries have ineffective intellectual property strategy; engineers do little patent searching, face challenges to identify novel product features, and often find difficulties to interpret patent information. The initial experiments of using the crowdsourcing approach for patent clustering activity reveal that general crowd workers (not knowing much about patents) were able to identify one third of expert clustered schema for much lesser cost. Further research work to strengthen the usefulness of the crowdsourcing approach for patent landscaping related activities is discussed

The generation of problem-focussed patent clusters: a comparative analysis of crowd intelligence with algorithmic and expert approaches

This paper presents a new crowdsourcing approach to the construction of patent clusters, and systematically benchmarks it against previous expert and algorithmic approaches. Patent databases should be rich sources of inspiration which could lead engineering designers to novel solutions for creative problems. However, the sheer volume and complexity of patent information means that this potential is rarely realised. Rather than the keyword driven searches common in commercial systems, designers need tools that help them to understand patents in the context of the problem they are considering. This paper presents an approach to address this problem by using crowd intelligence for effective generation of patent clusters at lower cost and with greater rationale. A systematic study was carried out to compare the crowd’s efficiency with both expert and algorithmic patent clusters, with the results indicating that the crowd was able to create 80% more patent pairs with appropriate rationale

Realising the affective potential of patents: a new model of database interpretation for user-centred design

This research sets out a new interpretation of the patent database using affective design parameters. While this resource contains a vast quantity of technical information, its extraction and use in practical design settings is extremely challenging. Until now, all filing and subsequent landscaping or profiling of patents has been based on their technical characteristics. We set out an alternative approach that utilises crowdsourcing to first summarise patents and then applies text analysis tools to assess the summarising text in relation to three affective parameters: appearance, ease of use, and semantics. The results been used to create novel patent clusters that provide an alternative perspective on relevant technical data, and support user-centric engineering design. The workflow and tasks to effectively interface with the crowd are outlined, and the process for harvesting and processing responses using a combination of manual and computational analysis is reviewed. The process creates sets of descriptive words for each patent which differ significantly from those created using only functional requirements, and support a new paradigm for the use of big data in engineering design – one that utilises desirable affective qualities as the basis for scouring and presenting relevant functional patent information for concept generation and development

Crowdsourcing solutions to 2D irregular strip packing problems from Internet workers

Many industrial processes require the nesting of 2D profiles prior to the cutting, or stamping, of components from raw sheet material. Despite decades of sustained academic effort algorithmic solutions are still sub-optimal and produce results that can frequently be improved by manual inspection. However the Internet offers the prospect of novel ‘human-in-the-loop’ approaches to nesting problems, that uses online workers to produce packing efficiencies beyond the reach of current CAM packages. To investigate the feasibility of such an approach this paper reports on the speed and efficiency of online workers engaged in the interactive nesting of six standard benchmark datasets. To ensure the results accurately characterise the diverse educational and social backgrounds of the many different labour forces available online, the study has been conducted with subjects based in both Indian IT service (i.e. Rural BPOs) centres and a network of homeworkers in northern Scotland. The results (i.e. time and packing efficiency) of the human workers are contrasted with both the baseline performance of a commercial CAM package and recent research results. The paper concludes that online workers could consistently achieve packing efficiencies roughly 4% higher than the commercial based-line established by the project. Beyond characterizing the abilities of online workers to nest components, the results also make a contribution to the development of algorithmic solutions by reporting new solutions to the benchmark problems and demonstrating methods for assessing the packing strategy employed by the best workers

Social Implications of Crowdsourcing in Rural Scotland

Various surveys mentioned that the commercial benefits of Internet crowdsourcing are reaped largely by people located in metro areas and smaller cities. The impact of crowdsourcing on the rural population is questionable. The aim of this research is to bridge widening urban and rural divide by providing knowledge-intensive crowdsourcing tasks to rural work force which could provide long term benefits to them as well as improve supporting infrastructure. This paper reports an initial study of the demographic of small samples of twenty two rural homeworkers in Scotland, their motivation to do crowdsourcing work, present main occupation, computer skills, views on rural infrastructure and finally their level of skill in solving three spatial visualization tests. The survey shows that flexible hours of working, extra income, and work life balance are the three important factors emphasized as motivational constructs to do crowdsourcing work. Their skills on solving a spatial visualization test is equivalent to the literature reported results, and also high correlations are identified between these tests. These results suggest that with minimum training the homeworkers could able to solve knowledge-intensive industrial spatial reasoning problems to increase their earning potentials

Social Implications of Crowdsourcing in Rural Scotland

Various surveys mentioned that the commercial benefits of Internet crowdsourcing are reaped largely by people located in metro areas and smaller cities. The impact of crowdsourcing on the rural population is questionable. The aim of this research is to bridge widening urban and rural divide by providing knowledge-intensive crowdsourcing tasks to rural work force which could provide long term benefits to them as well as improve supporting infrastructure. This paper reports an initial study of the demographic of small samples of twenty two rural homeworkers in Scotland, their motivation to do crowdsourcing work, present main occupation, computer skills, views on rural infrastructure and finally their level of skill in solving three spatial visualization tests. The survey shows that flexible hours of working, extra income, and work life balance are the three important factors emphasized as motivational constructs to do crowdsourcing work. Their skills on solving a spatial visualization test is equivalent to the literature reported results, and also high correlations are identified between these tests. These results suggest that with minimum training the homeworkers could able to solve knowledge-intensive industrial spatial reasoning problems to increase their earning potentials