Integrated Reconfigurable Autonomous Architecture System

Advances in state-of-the-art architectural robotics and artificially intelligent design algorithms have the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment. This system is situated within a larger body of research related to embedding autonomous agency directly into the built environment through the linkage of AI, computation, and robotics. It challenges the traditional separation between digital design and physical construction through the development of an autonomous architecture with an adaptive lifecycle. Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bi-directional cyber-physical control protocols for simultaneous state alignment. The generative algorithm is a multi-agent system trained using deep reinforcement learning to learn adaptive policies for adjusting the scales, shapes, and relational organization of spatial volumes by processing changes in the environment and user requirements. The robotic material system was designed with a symbiotic relationship between active and passive modular components. Distributed robots slide their bodies on tracks built into passive blocks that enable their locomotion while utilizing a locking and unlocking system to reconfigure the assemblages they move across. The three subsystems have been developed in relation to each other to consider both the constraints of the AI-driven design algorithm and the robotic material system, enabling intelligent spatial adaptation with a continuous feedback chain

Path Prediction For Efficient Order Release In Matrix-Structured Assembly Systems

Numerous research papers have already demonstrated the theoretical benefits of matrix-structured assembly systems. Nevertheless, such assembly systems have hardly been used in practice so far. The main reason for this, apart from the technical integration, is the complexity of controlling matrix-structured assembly systems. In theory, decentralized, agent-based control architectures have proven to be particularly suitable. However, order release has been largely neglected so far. Accordingly, the authors' previous work includes a conceptual approach for capacity-oriented order release in matrix-structured assembly systems. This previous approach calculates possible paths of an order and their capacity requirements considering both routing and sequence flexibility. Furthermore, by combining the possible paths of released orders with orders to be released and comparing them with the available capacity, the previously suggested approach can systematically carry out capacity-oriented release decisions. However, the NP-hard (NP: non-deterministic polynomial-time) problem arising from the consideration of all possible paths has a negative impact on the scalability and real-time capability of order release. Therefore, the present paper aims to extend the previously developed approach. By determining the most likely paths that a given order will take through the assembly system, the combination possibilities are limited in such a way that the total amount of calculations required to find a suitable order for release is reduced. Doing so, the NP-hardness of the previously developed approach can be circumvented. This work contributes to the practical realization and economic operation of matrix-structured assembly systems. The paper describes the logic of path prediction in detail and evaluates its impact on order release

New Perspectives in Manufacturing: An Assessment for an Advanced Reconfigurable Machining System

Traditionally manufacturing cycle involves several production processes that are carried out according to the required technologies tacking into account the constraint due to the production capacity provided by machine tools and the customers' orders time schedule In this paper, a new modular, reconfigurable and scalable machining centre is presented. The resulting system is characterized by the possibility of modifying the machining capacity as well as exchanging the role between workpieces and machining/operating resources. This augmented flexibility creates new opportunities for efficient manufacturing; however, the increased system complexity demands a new approach for the jobs scheduling and machining control. An architecture based on agents modelling is proposed and discussed

A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.This work was financed by MINECO/FEDER, UE (grant number DPI2015-68602-R) and by UPV/EHU (grant number PPG17/56)

Using Multi-agent Systems to Pursue Autonomy with Automated Components

AbstractHumans have used tools to transform raw resources into valued outputs ever since society harnessed fire. The type of tool, amount of effort and form of energy required depends on the output or object being created. As tools evolved into machines, they enhanced operator productivity. Hence, industry continues to invest heavily in machines to assist people to do more with less physical control and/or interaction. This involves automating functions previously completed manually. Taylorism and the Hawthorn experiments all contributed to optimising industrial outputs and value engineers continue to promote a mecha- nized workforce in order to minimise business variations in human performance and their behaviour. Researchers have also pursued this goal using Computational Intelligence (CI) techniques. This process of transforming cognitive functionality into machine actionable form has encompassed many careers. Machine Intelligence (MI) is becoming more aspirational, with Artificial Intelligence (AI) enabling the achievement of numerous goals. More recently, Multi-Agent Systems (MASs) have been employed to provide a flexible framework for research and development. These frameworks facilitate the development of component interoperability, with coordination and cooperation techniques needed to solve real-world problems. However problems typically manifest in complex, dynamic and often hostile environments. Based on the effort to seek or facilitate human-like decision making within machines, it is clear that further research is required. This paper discusses one possible avenue. It involves future research, aimed at achieving a cognitive sub-system for use on-board platforms. The framework is introduced by describing the human-machine relationship, followed by the theoretic background into cognitive architectures and a conceptual mechanism that could be used to implement a virtual mind. One which could be used to improve automation, achieve greater independence and enable more autonomous behaviour within control systems

Collaborative and adaptive supply chain planning

Dans le contexte industriel d'aujourd'hui, la compétitivité est fortement liée à la performance de la chaîne d'approvisionnement. En d'autres termes, il est essentiel que les unités d'affaires de la chaîne collaborent pour coordonner efficacement leurs activités de production, de façon a produire et livrer les produits à temps, à un coût raisonnable. Pour atteindre cet objectif, nous croyons qu'il est nécessaire que les entreprises adaptent leurs stratégies de planification, que nous appelons comportements, aux différentes situations auxquelles elles font face. En ayant une connaissance de l'impact de leurs comportements de planification sur la performance de la chaîne d'approvisionnement, les entreprises peuvent alors adapter leur comportement plutôt que d'utiliser toujours le même. Cette thèse de doctorat porte sur l'adaptation des comportements de planification des membres d'une même chaîne d'approvisionnement. Chaque membre pouvant choisir un comportement différent et toutes les combinaisons de ces comportements ayant potentiellement un impact sur la performance globale, il est difficile de connaître à l'avance l'ensemble des comportements à adopter pour améliorer cette performance. Il devient alors intéressant de simuler les différentes combinaisons de comportements dans différentes situations et d'évaluer les performances de chacun. Pour permettre l'utilisation de plusieurs comportements dans différentes situations, en utilisant la technologie à base d'agents, nous avons conçu un modèle d'agent à comportements multiples qui a la capacité d'adapter son comportement de planification selon la situation. Les agents planificateurs ont alors la possibilité de se coordonner de façon collaborative pour améliorer leur performance collective. En modélisant les unités d'affaires par des agents, nous avons simulé avec la plateforme de planification à base d'agents de FORAC des agents utilisant différents comportements de planification dits de réaction et de négociation. Cette plateforme, développée par le consortium de recherche FORAC de l'Université Laval, permet de simuler des décisions de planification et de planifier les opérations de la chaîne d'approvisionnement. Ces comportements de planification sont des métaheurisciques organisationnelles qui permettent aux agents de générer des plans de production différents. La simulation est basée sur un cas illustrant la chaîne d'approvisionnement de l'industrie du bois d'œuvre. Les résultats obtenus par l'utilisation de multiples comportements de réaction et de négociation montrent que les systèmes de planification avancée peuvent tirer avantage de disposer de plusieurs comportements de planification, en raIson du contexte dynamique des chaînes d'approvisionnement. La pertinence des résultats de cette thèse dépend de la prémisse que les entreprises qui adapteront leurs comportements de planification aux autres et à leur environnement auront un avantage concurrentiel important sur leurs adversaires

Design agency:prototyping multi-agent systems in architecture

This paper presents research on the prototyping of multi-agent systems for architectural design. It proposes a design exploration methodology at the intersection of architecture, engineering, and computer science. The motivation of the work includes exploring bottom up generative methods coupled with optimizing performance criteria including for geometric complexity and objective functions for environmental, structural and fabrication parameters. The paper presents the development of a research framework and initial experiments to provide design solutions, which simultaneously satisfy complexly coupled and often contradicting objectives. The prototypical experiments and initial algorithms are described through a set of different design cases and agents within this framework; for the generation of façade panels for light control; for emergent design of shell structures; for actual construction of reciprocal frames; and for robotic fabrication. Initial results include multi-agent derived efficiencies for environmental and fabrication criteria and discussion of future steps for inclusion of human and structural factors