Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria

Deployment of Heterogeneous Swarm Robotic Agents Using a Task-Oriented Utility-Based Algorithm

In a swarm robotic system, the desired collective behavior emerges from local decisions made by robots, themselves, according to their environment. Swarm robotics is an emerging area that has attracted many researchers over the last few years. It has been proven that a single robot with multiple capabilities cannot complete an intended job within the same time frame as that of multiple robotic agents. A swarm of robots, each one with its own capabilities, are more flexible, robust, and cost-effective than an individual robot. As a result of a comprehensive investigation of the current state of swarm robotic research, this dissertation demonstrates how current swarm deployment systems lack the ability to coordinate heterogeneous robotic agents. Moreover, this dissertation's objective shall define the starting point of potential algorithms that lead to the development of a new software environment interface. This interface will assign a set of collaborative tasks to the swarm system without being concerned about the underlying hardware of the heterogeneous robotic agents. The ultimate goal of this research is to develop a task-oriented software application that facilitates the rapid deployment of multiple robotic agents. The task solutions are created at run-time, and executed by the agents in a centralized or decentralized fashion. Tasks are fractioned into smaller sub-tasks which are, then, assigned to the optimal number of robots using a novel Robot Utility Based Task Assignment (RUTA) algorithm. The system deploys these robots using it's application program interfaces (API's) and uploads programs that are integrated with a small routine code. The embedded routine allows robots to configure solutions when the decentralized approach is adopted. In addition, the proposed application also offers customization of robotic platforms by simply defining the available sensing and actuation devices. Another objective of the system is to improve code and component reusability to reduce efforts in deploying tasks to swarm robotic agents. Usage of the proposed framework prevents the need to redesign or rewrite programs should any changes take place in the robot's platform

Skill-based reconfiguration of industrial mobile robots

Caused by a rising mass customisation and the high variety of equipment versions, the exibility of manufacturing systems in car productions has to be increased. In addition to a exible handling of production load changes or hardware breakdowns that are established research areas in literature, this thesis presents a skill-based recon guration mechanism for industrial mobile robots to enhance functional recon gurability. The proposed holonic multi-agent system is able to react to functional process changes while missing functionalities are created by self-organisation. Applied to a mobile commissioning system that is provided by AUDI AG, the suggested mechanism is validated in a real-world environment including the on-line veri cation of the recon gured robot functionality in a Validity Check. The present thesis includes an original contribution in three aspects: First, a recon - guration mechanism is presented that reacts in a self-organised way to functional process changes. The application layer of a hardware system converts a semantic description into functional requirements for a new robot skill. The result of this mechanism is the on-line integration of a new functionality into the running process. Second, the proposed system allows maintaining the productivity of the running process and exibly changing the robot hardware through provision of a hardware-abstraction layer. An encapsulated Recon guration Holon dynamically includes the actual con guration each time a recon guration is started. This allows reacting to changed environment settings. As the resulting agent that contains the new functionality, is identical in shape and behaviour to the existing skills, its integration into the running process is conducted without a considerable loss of productivity. Third, the suggested mechanism is composed of a novel agent design that allows implementing self-organisation during the encapsulated recon guration and dependability for standard process executions. The selective assignment of behaviour-based and cognitive agents is the basis for the exibility and e ectiveness of the proposed recon guration mechanism

Asset selection and optimisation for robotic assembly cell reconfiguration

With the development of Industry 4.0, the manufacturing industry has revolutionized a lot. Product manufacture becomes more and more customized. This trend is achieved by innovative techniques, such as the reconfigurable manufacturing system. This system is designed at the outset for rapid change in its structure, as well as in software and hardware components, to respond to market changes quickly. Robots are important in these systems because they provide the agility and precision required to adapt rapidly to new manufacturing processes and customization demands. Despite the importance of applying robots in these systems, there might be some challenges. For example, there is data from multiple sources, such as the technical manual sensor data. Besides, robot applications must react quickly to the ever-changing process requirements to meet customer's requirements. Furthermore, further optimization, especially layout optimization, is needed to ensure production efficiency after adaptation to the current process requirements. To address these challenges, this doctoral thesis presents a framework for reconfiguring robotic assembly cells in manufacturing. This framework consists of three parts: the experience databank, the methodology for optimal manufacturing asset selection, and the methodology for layout optimization. The experience databank is introduced to confront the challenge of assimilating and processing heterogeneous data from numerous manufacturing sources, which is achieved by proposing a vendor-neutral ontology model. This model is specifically designed for encapsulating information about robotic assembly cells and is subsequently applied to a knowledge graph. The resulting knowledge graph, constituting the experience databank, facilitates the effective organization and interpretation of the diverse data. An optimal manufacturing asset selection methodology is introduced to adapt to shifting processes and product requirements, which focuses on identifying potential assets and their subsequent evaluation. This approach integrates a modular evaluation framework that considers multiple criteria such as cost, energy consumption, and robot maneuverability, ensuring the selection process remains robust in changing market demands and product requirements. A scalable methodology for layout optimization within the reconfigurable robotic assembly cells is proposed to resolve the need for further optimization post-adaption. It introduces a scalable, multi-decision modular optimization framework that synergizes a simulation environment, optimization environment, and robust optimization algorithms. This strategy utilizes the insights garnered from the experience databank to facilitate informed decision-making, thereby enabling the robotic assembly cells to not only meet the immediate production exigencies but also align with the manufacturing landscape's evolving dynamics. The validation of the three methodologies presented in this doctoral thesis encompasses both software development and practical application through three distinct use cases. For the experience databank, an interface was developed using Protégé, Neo4j, and Py2neo, allowing for effective organization and processing of varied manufacturing data. The programming interface for the asset selection methodology was built using Python, integrating with the experience databank via Py2neo and Neo4j to facilitate dynamic and informed decision-making in asset selection. In terms of software for the layout optimization framework, two different applications were developed to demonstrate the framework's scalability and adaptability. The first application, combining Python and C# programming with Siemens Tecnomatix Process Simulate, is geared towards optimizing layouts involving multiple machines. The second application utilizes Python programming alongside the RoboDK API and RoboDK software, tailored for layout optimization in scenarios involving a single robot. Complementing these software developments, the methodologies were further validated through three use cases, each addressing a unique aspect of the framework. Use Case 1 focused on implementing asset selection and system layout optimization based on a single objective, leveraging the experience databank. The required assets are selected, and the required cycle time for executing the whole robotic assembly operation has been reduced by 15.6% from 47.17 seconds to 39.83 seconds. Use Case 2 extended the layout optimization to single-robot operations with an emphasis on multi-criteria decision-making. The energy consumption was minimized to 5613.59 Wh after implementing optimization strategies, demonstrating a significant enhancement in energy efficiency. Compared with the baseline of 6164.98 Wh, this represents an 8.9% reduction in energy usage. For minimized cycle time, a reduction of 6.0% from the baseline of 57.11 seconds is achieved, resulting in a cycle time of 53.15 seconds. Regarding the pursuit of a maximized robot maneuverability index, an increase of 140.8% from the baseline of 0.4891235 is achieved, resulting in a maximized value of 1.1786125. Lastly, Use Case 3 tested the modular and multi-objective asset selection methodology, demonstrating its efficacy across diverse operational scenarios. Evaluations conducted with two multi-objective optimization algorithms, Non-Dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm II, revealed interesting implications for selecting and optimizing robotic assets in response to new customer requests. Specifically, Strength Pareto Evolutionary Algorithm II identified a Pareto solution that was more cost-effective (£20,920) compared to Non-Dominated Sorting Genetic Algorithm II (£21,090), while maintaining a competitive specification efficiency score (0.865 vs. 0.879). Consequently, Strength Pareto Evolutionary Algorithm II is preferred for optimizing robotic asset selection in scenarios prioritizing cost. However, should the requirement shift towards maximizing specification efficiency, the Non-Dominated Sorting Genetic Algorithm II would be the more suitable choice. These use cases not only showcased the practical applicability of the developed software but also underlined the robustness and adaptability of the proposed methodologies in real-world manufacturing environments. In conclusion, this doctoral thesis presents a methodology for reconfiguring robotic assembly cells in manufacturing. By harnessing the capabilities of artificial intelligence, knowledge graphs, and simulation methodologies, it addresses the challenges of processing data from diverse sources, adapting to fluctuating market demands, and establishing further optimizations for enhanced operational efficiency in the modern manufacturing landscape. To affirm the viability of this framework, the thesis integrates software development procedures tailored to the proposed methodologies and furnishes evidence through three use cases, which are evaluated against well-defined criteria