Reinforcement Learning Approaches to Biological Manufacturing Systems

Ueda, Kanji; Hatono, Itsuo; Fujii, Nobutada; Vaario, Jari

doi:10.1016/s0007-8506(07)62960-6

Cited by 66 publications

(24 citation statements)

References 7 publications

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“…The main focus of this concept is to deal autonomously with dynamic and unpredictable changes in internal and external production environments by changing a system's configuration. In order to implement the concept of a BMS, methods of evolutionary computation, self-organization, and reinforcement learning were developed [17], [18], [20].…”

Section: B Evolution Of Planning Systems 1) Biological Manufacturingmentioning

confidence: 99%

Artificial Cognition in Production Systems

Bannat¹,

Bautze²,

Beetz³

et al. 2011

IEEE Trans. Automat. Sci. Eng.

103

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Abstract-Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally-and situationally dependent triggered paths on state-based graphs are described in this paper.Note to Practitioners-Today's manufacturing enterprises have to face a number of challenges in a turbulent environment that originates amongst other from saturated markets, unprecedented and abrupt changes in market demands, an ever increasing number of product variants and smaller lot sizes. A recent research trend in Germany is the so called Cognitive Factory, where artificial cognitive capabilities are introduced to the control of production systems. The applications range from production planning and control, human-robot-cooperation, automatic robot programming to intuitive worker guidance systems.The concept of fully automated production systems is no longer a viable vision, as it has been shown, that the conventional automa- tion is not able to deal with the ever-rising complexity of modern production systems. Especially, a high reactivity, agility and adaptivity that is required by modern production systems, can only be reached by human operators with their immense cognitive capabilities, which enable them to react to unpredictable situations, to plan their further actions, to learn and to gain experience and to communicate with others. Thus, new concepts are required, that apply these cognitive principles to the planning processes and control systems of pr...

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Section: B Evolution Of Planning Systems 1) Biological Manufacturingmentioning

confidence: 99%

Artificial Cognition in Production Systems

Bannat¹,

Bautze²,

Beetz³

et al. 2011

IEEE Trans. Automat. Sci. Eng.

103

View full text Add to dashboard Cite

show abstract

“…A parallel trend was making some explored mechanisms of Nature operational in the organization and control of manufacturing systems. This approach resulted in the socalled biological manufacturing systems [35] [58] [59] and later on, led to the engineering's concept of emergence [28].…”

Section: Organization Patterns In Manufacturingmentioning

confidence: 99%

Agent-Based Systems for Manufacturing

2006

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The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

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“…A biological manufacturing system [60,61] with highly dynamic characteristics and high adaptability is created based on the mechanisms used in material cycling, sustainability, and transmission of genetic information in the biosphere. A highly adaptive, intelligent, and real-time system called "biologically inspired manufacturing system" [62] has been recently developed based on the ultra-short feedback path in the biological nervous system and has the ability to respond rapidly to the changes occurring in a production process.…”

Section: Product or System Development Based On Biological Prototypesmentioning

confidence: 99%

Creative design inspired by biological knowledge: Technologies and methods

et al. 2018

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Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions.

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Reinforcement Learning Approaches to Biological Manufacturing Systems

Cited by 66 publications

References 7 publications

Artificial Cognition in Production Systems

Artificial Cognition in Production Systems

Agent-Based Systems for Manufacturing

Creative design inspired by biological knowledge: Technologies and methods

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