Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics

Kirks, Thomas; Jost, Jana; Uhlott, Tim; Jakobs, Matthias

doi:10.1109/itsc.2018.8569949

Cited by 6 publications

(10 citation statements)

References 10 publications

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“…All this entities are represented by agents which contain the functionalities and characteristics of their physical entity as well as behaviors and the ability to exchange data with one another in specific forms of protocols [13]. For sharing information and teaming up, the human can also be integrated in such a multi-agent system by representing him with all his abilities and properties as an agent [2]. In addition it makes sense to model multi-agent systems as a hybrid of conventional MAS and service-oriented architectures as stated by Iñigo-Blasco et al [14].…”

Section: Decentralized Control Systems Organizationmentioning

confidence: 99%

“…For these scenarios localization, identification and the team integration play a major role. Kirks et al [2] showed how to retrieve absolute pose of the worker for interaction, we also want to use this information and provide it to other non-locatable objects in warehouse logistics. An ATV, simply following an optical line, does not know its absolute pose in an environment and still can fulfil transport tasks.…”

Section: Adaptive Interaction In Logistics Environmentsmentioning

confidence: 99%

“…Such complex cyber-physical production systems (CPPS) arise in the Industry 4.0 and can be seen as socio-technical systems in which humans and technologies are working close together [1]. First robotic systems occur which focus on the acceptance of the human by offering natural interaction [2]. While the technologies adapt to the human, the information about the worker is often very limited.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics

Kirks

Jost

Uhlott

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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The approach of this paper is to investigate the application of smart glasses for human-robot interaction for scenarios like warehouse logistics, transportation systems and logistics in general. Following various research questions we conduct an experiment with multiple tests to gather information on the accuracy of localization with the Microsoft HoloLens and the feasibility of its application. The tests can also be taken into account when testing other wearables for the same purpose. An adequate accuracy enables the human worker to be integrated in decentralized control systems better and build teams of the diverse entities in human-robot interaction and cooperation.

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Section: Decentralized Control Systems Organizationmentioning

confidence: 99%

Section: Adaptive Interaction In Logistics Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics

Kirks

Jost

Uhlott

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…Recently, Industry 4.0 applications have increasingly been applied to intralogistics. Such applications, also called smart intralogistics systems, comprise an ecosystem of interconnected active agents that can de‐centrally coordinate information, make optimal real‐time decisions, and execute meaningful actions with minimal or no human supervision (Kirks et al, 2018). Trends in smart intralogistics systems include autonomous and IoT‐driven technologies such as AMRs, 3D‐shuttle systems, intrafacility drones, self‐organizing conveyor systems, collaborative robots, smart container systems, grid sorters, real‐time simulations, wearable smart exoskeletons, and so forth (Winkler & Zinsmeister, 2019).…”

Section: Introductionmentioning

confidence: 99%

Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

Grover,

Ashraf

2024

Transp. j.

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Eyeing superior operational performance, transportation, and warehousing management, researchers have turned their much‐needed attention to autonomous and IoT‐driven intralogistics systems. Despite its potential, a systematic evaluation of its overall business needs and the criteria for its success at each stage of adoption is missing in the literature. Using the business analysis framework, augmented by the technology adoption model, this study seeks to provide the business context for adopting autonomous and IoT‐driven intralogistics by identifying business requirements and critical success factors for such systems. We thematically analyze 85 recent research articles on autonomous and IoT‐driven intralogistics systems to identify business requirements that are linked to the mission of maximizing operational profit for the warehousing and storage industry. Then, using the identified business requirements as a base, we thematically analyze those 85 research articles again to identify critical success factors at different stages of technology adoption, namely information, analysis, acquisition, and utilization. We use the findings to develop propositions for future researchers. These findings provide a foundation for developing empirical, descriptive, and normative research on adopting and managing these systems for the warehousing and storage industry.

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“…Logistics is the process of planning and organizing to make sure that resources are in the places where they are needed, while intralogistics is the management and optimization of production and distribution processes in an internal place such as a warehouse. Logistics is how to move products from one point to another point, and intralogistics is the same concept, but more related to how to get products most efficiently from the receiving place to the shipping place in a warehouse, a factory, or a plant [ 1 , 2 , 3 ]. The globalization and high demand of electronic commerce cause increasingly complex material handling systems, while market predictions are less reliable [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System

Kim

Choi

Hwang

et al. 2020

Sensors

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Intralogistics is a technology that optimizes, integrates, automates, and manages the logistics flow of goods within a logistics transportation and sortation center. As the demand for parcel transportation increases, many sortation systems have been developed. In general, the goal of sortation systems is to route (or sort) parcels correctly and quickly. We design an n-grid sortation system that can be flexibly deployed and used at intralogistics warehouse and develop a collaborative multi-agent reinforcement learning (RL) algorithm to control the behavior of emitters or sorters in the system. We present two types of RL agents, emission agents and routing agents, and they are trained to achieve the given sortation goals together. For the verification of the proposed system and algorithm, we implement them in a full-fledged cyber-physical system simulator and describe the RL agents’ learning performance. From the learning results, we present that the well-trained collaborative RL agents can optimize their performance effectively. In particular, the routing agents finally learn to route the parcels through their optimal paths, while the emission agents finally learn to balance the inflow and outflow of parcels.

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Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics

Cited by 6 publications

References 10 publications

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics

Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System

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