Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion

In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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“…Different sensing techniques provide different aspects for varying interests [130]. For example, Fig.…”

Section: Smart Sensor Network and Sensor Data Fusionmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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“…RL is used in the estimation of factors such as human kinematics via a recursive least-square (RLS) algorithm. In a more comprehensive use of AI in monitoring [88], a deep learning-based multimodal fusion architecture is developed for the robust operation of an HRC manufacturing system. Three modes of communication between humans and robots are considered in this work: voice, hand motion, and body motion.…”

Section: Operatementioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

236

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Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

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“…However, achieving natural interaction is not a simple task because it requires continuous monitoring of the surroundings and surveilling the communication flow direction to assure safety while interacting in the communication [58]. Researchers defend several approaches such as voice commanding, gesture recognition [59], collision avoidance and human-aware navigation, among other solutions to achieve natural interaction [1], [29], [60]- [62].…”

Section: ) Operation Levelmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shop-floor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.

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Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion

Cited by 71 publications

References 42 publications

Symbiotic human-robot collaborative assembly

Symbiotic human-robot collaborative assembly

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

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