Advanced Researches in Multiple Humans–Machine(s) Collaboration for Industrial Applications

Topic Information

Dear Colleagues,

Manufacturing is evolving rapidly—becoming more automated, connected, digital, and intelligent—by merging physical and virtual spaces. Industrial robots play a key role in this manufacturing transformation since they can be monitored and controlled in real-time to perform various “physical” jobs instead of, or increasingly along with, human workers. Despite the fact that more industrial robots are entering the workplace, there is a growing manufacturing skills gap for many reasons, such as an aging workforce and lack of work–life balance. Great advances have been made on the collaboration of one human and one telerobot collaboration in an unknown or complex situation. The teleoperator ideally can leverage the strengths of both humans (e.g., flexibility, decision-making skills, etc.) and robots (e.g., strength, accuracy, etc.). Extending one-human–one-robot collaboration to embrace multiple-humans–one/multiple-robots collaborations may bring additive or synergistic benefits. These collaborative opportunities also require re-thinking how humans and robots form a partnership to complete a complicated “physical” task based on such a collaboration.

The goal of this Special Issue thus is to document and highlight recent progress and future opportunities related to the collaboration/interaction of multiple-humans–one/multiple-robots for industrial applications, with a focus on human performance, safety, and multiple-humans–one/multiple-robots work processes.  

Topics of interest for this Special Issue include, but are not limited to, the following:

• Multiple humans–robot(s) collaboration;
• Humans–robot(s) collaboration in distributed workplaces;
• Intention sharing between humans and between humans and robots;
• Design and framework of remote and/or wearable sensing systems for multiple humans–robot(s) collaboration;
• AI and machine learning for multiple humans–robot(s) collaboration;
• Trust in humans–robot partnership;
• Cooperative control and joint-decision making of humans–robot(s) teams in an industry setting;
• Safety in multiple humans–robot(s) collaboration.

Dr. Sunwook Kim
Dr. Sol Lim
Prof. Dr. Lihui Wang
Dr. Sotiris Makris
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Automation automation	-	-	2020	26.3 Days	CHF 1000
Machines machines	2.6	2.1	2013	15.6 Days	CHF 2400
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600
Systems systems	1.9	3.3	2013	16.8 Days	CHF 2400

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Published Papers (2 papers)

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All Automation Machines Robotics Sensors Systems

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21 pages, 4837 KiB  

Open AccessArticle

Unified Human Intention Recognition and Heuristic-Based Trajectory Generation for Haptic Teleoperation of Non-Holonomic Vehicles

by Panhong Zhang, Tao Ni, Zeren Zhao and Changan Ren

Machines 2023, 11(5), 528; https://doi.org/10.3390/machines11050528 - 04 May 2023

Viewed by 1208

Abstract

In this paper, a novel bilateral shared control approach is proposed to address the issue of strong dependence on the human, and the resulting burden of manipulation, in classical haptic teleoperation systems for vehicle navigation. A Hidden Markov Model (HMM) is utilized to [...] Read more.

In this paper, a novel bilateral shared control approach is proposed to address the issue of strong dependence on the human, and the resulting burden of manipulation, in classical haptic teleoperation systems for vehicle navigation. A Hidden Markov Model (HMM) is utilized to handle the Human Intention Recognition (HIR), according to the force input by the human—including the HMM solution, i.e., Baum–Welch algorithm, and HMM decoding, i.e., Viterbi algorithm—and the communication delay in teleoperation systems is added to generate a temporary goal. Afterwards, a heuristic and sampling method for online generation of splicing trajectory based on the goal is proposed innovatively, ensuring the vehicle can move feasibly after the change in human intention is detected. Once the trajectory is available, the vehicle velocity is then converted to joystick position information as the haptic cue of the human, which enhances the telepresence. The shared teleoperation control framework is verified in the simulation environment, where its excellent performance in the complex environment is evaluated, and its feasibility is confirmed. The experimental results show that the proposed method can achieve simple and efficient navigation in a complex environment, and can also give a certain situational awareness to the human. Full article

(This article belongs to the Topic Advanced Researches in Multiple Humans–Machine(s) Collaboration for Industrial Applications)

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31 pages, 8226 KiB  

Open AccessArticle

Digital Transformation Journey Guidance: A Holistic Digital Maturity Model Based on a Systematic Literature Review

by Arzu Aras and Gülçin Büyüközkan

Systems 2023, 11(4), 213; https://doi.org/10.3390/systems11040213 - 20 Apr 2023

Cited by 6 | Viewed by 6461

Abstract

For a successful digital transformation, organizations must create an accurate roadmap and manage the process effectively. A digital maturity model is a critical success factor as it enables organizations to assess their current situation and create roadmaps aligned with their goals; however, a [...] Read more.

For a successful digital transformation, organizations must create an accurate roadmap and manage the process effectively. A digital maturity model is a critical success factor as it enables organizations to assess their current situation and create roadmaps aligned with their goals; however, a comprehensive systematic literature review covering the maturity models proposed by academia and consultancy firms is hard to find. Further, the existing models are sector-oriented, not organization-oriented, and do not consider the transformation journey holistically, but instead focus on model dimensions. This study first undertakes a comprehensive and up-to-date systematic literature review by applying the PRISMA approach using a bibliometric analysis tool capable of providing visual maps, then developing a unique holistic digital maturity model that covers several aspects of an organization’s digital transformation journey, from strategy to governance, and asking relevant questions. The hierarchical structure, comprising dimensions and sub-dimensions, presents content beyond the scope of other models. The results of the digital maturity assessment can be interpreted in parallel with the stages of the digital transformation. Consequently, the new holistic and sector-independent digital maturity model can be used by organizations in both the private and public sector. Full article

(This article belongs to the Topic Advanced Researches in Multiple Humans–Machine(s) Collaboration for Industrial Applications)

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