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

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

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

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 RTEA’s 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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of a front-end system for customised product specification in the ManuCloud

The objective of the ManuCloud project is the development of a service-oriented IT environment as a basis for the next level of manufacturing networks by enabling production-related inter-enterprise integration down to shop floor level. Industrial relevance is guaranteed by involving industrial partners from the photovoltaic, organic lightning and automotive supply industries

A product configurator for cloud manufacturing

Product configurators have become an important enabler for enterprises to achieve product customization in order to address individual customers’ requirements. Despite adoption across a wide range of application domains from automotive to consumer goods, even state-of-the-art product configuration systems are limited in their ability to quickly respond to changes in the production systems that deliver the goods specified. Enabled by the emerging paradigm of cloud manufacturing, the authors propose a “configurable configurator” that is automatically updated to reflect changes in the supply chain. The paper reports the ongoing research and development towards a dynamically generated system that supports product configuration, visualization and assessment from the cloud manufacturing concept of Manufacturing-as-a-Service (MaaS). In addition to outlining the architecture of such a system, an overview of its modules and integration to the cloud manufacturing platform is described. Lastly, the case study of a customizable façade module is presented with two different scenarios to demonstrate the prototype implementation and validate the proposed approach

Towards crowdsourcing spatial manufacturing tasks from rural India

Many tasks involving production software that involve spatial reasoning can only produce "good" rather than optimum solutions. It is frequently possible for humans to improve on algorithmically generated solutions computed by CAD/CAM software. The thesis motivating this research is that large numbers of industrial optimisation tasks involving spatial reasoning (such as 2D part nesting) can be outsourced as human intelligence tasks to rural workers to provide a sustainable source of skilled employment. We hypothesis that 3D spatial reasoning ability is essential to solve spatial optimization tasks. In this paper we reported the 3D spatial manipulation ability of eighty rural workers in four rural business process outsourcing (BPO) centres in India. The assessments reveal that although the average spatial manipulation ability is less than the literature reported benchmark, there are talented workers identified in all the four rural centres, and the results identify priority activities required to enable the proposed approach

Putting the crowd to work in a knowledge-based factory

Although researchers have developed numerous computational approaches to reasoning and knowledge representation, their implementations are always limited to specific applications (e.g. assembly planning, fault diagnosis or production scheduling) for which bespoke knowledge bases or algorithms have been created. However, “cloud computing” has made irrelevant both the physical location and internal processes used by machine intelligence. In other words, the Internet encourages functional processes to be treated as ‘black boxes’ with which users need only be concerned with posing the right question and interpreting the response. The system asking the questions does not need to know how answers are generated, only that they are available in an appropriate time frame. This paper proposes that Crowdsourcing could provide on-line, ‘black-box’, reasoning capabilities that could far exceed the capabilities of current AI technologies (i.e. genetic algorithms, neural-nets, case-based reasoning) in terms of flexibility and scope. This paper describes how Crowdsourcing has been deployed in three different reasoning scenarios to carry out industrial tasks that involve significant amounts of tacit (e.g. unformalised) knowledge. The first study reports the application of Crowdsourcing to identify canonical view of 3D CAD models. The qualitative results suggest that the anonymous, Internet, workforce have a good comprehension of 3D geometry. Having established this basic competence the second experiment assesses the Crowd’s ability to judge the similarity of 3D components. Comparison of the results with published benchmarks shows a high degree of correspondence. Lastly the performance of the Internet labourers is quantified in a 2D nesting task, where their performance is found to be superior to reported computational algorithms. In all these cases results were returned within a couple of hours and the paper concludes that there is potential for broad application of Crowdsourcing to geometric problem solving in CAD/CAM