Scalable Representation Learning for Long-Term Augmented Reality-Based Information Delivery in Collaborative Human-Robot Perception

Han, Fei; Siva, Sriram; Zhang, Hao

doi:10.1007/978-3-030-21565-1_4

Cited by 3 publications

(1 citation statement)

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“…A structured light camera is used for color-depth data and a digital luminosity sensor for luminosity data. Similarly, in [ 42 ], the authors introduced a representation learning approach that learns a scalable long-term representation model, for scene matching. The features of multiple scene templates are learned and used to select, in an adaptable way, the most characteristic subset of templates to build the representation model for the current surrounding environment.…”

Section: Robotic Systems and Human-robot Perceptionmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

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Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators.

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Section: Robotic Systems and Human-robot Perceptionmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

View full text Add to dashboard Cite

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A review of external sensors for human detection in a human robot collaborative environment

Saleem,

Gustafsson,

Furey

et al. 2024

J Intell Manuf

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Manufacturing industries are eager to replace traditional robot manipulators with collaborative robots due to their cost-effectiveness, safety, smaller footprint and intuitive user interfaces. With industrial advancement, cobots are required to be more independent and intelligent to do more complex tasks in collaboration with humans. Therefore, to effectively detect the presence of humans/obstacles in the surroundings, cobots must use different sensing modalities, both internal and external. This paper presents a detailed review of sensor technologies used for detecting a human operator in the robotic manipulator environment. An overview of different sensors installed locations, the manipulator details and the main algorithms used to detect the human in the cobot workspace are presented. We summarize existing literature in three categories related to the environment for evaluating sensor performance: entirely simulated, partially simulated and hardware implementation focusing on the ‘hardware implementation’ category where the data and experimental environment are physical rather than virtual. We present how the sensor systems have been used in various use cases and scenarios to aid human–robot collaboration and discuss challenges for future work.

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