Safeguarding Collaborative Mobile Manipulators - Evaluation of the VALERI Workspace Monitoring System

Saenz, José; Vogel, Christian; Penzlin, Felix; Elkmann, Norbert

doi:10.1016/j.promfg.2017.07.129

Cited by 13 publications

(7 citation statements)

References 1 publication

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“…The images and videos coming from RGB-D cameras are also useful for either (i) configuring the motion constraints based on the human presence, differentiating human-type obstacles from the generic ones, or (ii) predicting the human activity, so the robot can react accordingly to the operator action [ 60 ]. A sensor system that provides reliable 2½D data for monitoring the working space is presented in [ 61 ]. The system elaborates data coming from three pairs of grayscale stereo vision cameras and a Time-of-Flight camera that monitors the motion of the human operator collaborating with the manipulator.…”

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

show abstract

“…In this way, the robot goal pose is set on the boundary of the safety zone to account for inaccurate human tracking. To determine more accurately the size of safety zones such as δ saf ety , in compliance with the requirements of ISO/TS 15066, one might refer to [22]. Moreover, the desired orientation is independent of human orientation.…”

Section: Fsm and Modes Switchingmentioning

confidence: 99%

A Visuo-Haptic Guidance Interface for Mobile Collaborative Robotic Assistant (MOCA)

Lamon

Fusaro

Balatti

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this work, we propose a novel visuo-haptic guidance interface to enable mobile collaborative robots to follow human instructions in a way understandable by nonexperts. The interface is composed of a haptic admittance module and a human visual tracking module. The haptic guidance enables an individual to guide the robot end-effector in the workspace to reach and grasp arbitrary items. The visual interface, on the other hand, uses a real-time human tracking system and enables autonomous and continuous navigation of the mobile robot towards the human, with the ability to avoid static and dynamic obstacles along its path. To ensure a safer human-robot interaction, the visual tracking goal is set outside of a certain area around the human body, entering which will switch robot behaviour to the haptic mode. The execution of the two modes is achieved by two different controllers, the mobile base admittance controller for the haptic guidance and the robot's whole-body impedance controller, that enables physically coupled and controllable locomotion and manipulation. The proposed interface is validated experimentally, where a human-guided robot performs the loading and transportation of a heavy object in a cluttered workspace, illustrating the potential of the proposed Follow-Me interface in removing the external loading from the human body in this type of repetitive industrial tasks.

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“…Meanwhile, sensor fusion is used to obtain more accurate odometer by combining the data from different sensors. A workspace monitoring system, which contains three stereo cameras and one time-of-flight camera, is used in the VALERY project for protecting the tool of mobile manipulator and has proven effective [9].…”

Section: ) Sensorsmentioning

confidence: 99%

Collaborative mobile industrial manipulator: A review of system architecture and applications

Yang

Zante

et al. 2019

2019 25th International Conference on Automation and Computing (ICAC)

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This paper provides a comprehensive review of the development of Collaborative Mobile Industrial Manipulator (CMIM), which is currently in high demand. Such a review is necessary to have an overall understanding about CMIM advanced technology. This is the first review to combine the system architecture and application which is necessary in order to gain a full understanding of the system. The classical framework of CMIM is firstly discussed, including hardware and software. Subsystems that are typically involved in hardware such as mobile platform, manipulator, end-effector and sensors are presented. With regards to software, planner, controller, perception, interaction and so on are also described. Following this, the common applications (logistics, manufacturing and assembly) in industry are surveyed. Finally, the trends are predicted and issues are indicated as references for CMIM researchers. Specifically, more research is needed in the areas of interaction, fully autonomous control, coordination and standards. Besides, experiments in real environment would be performed more and novel collaborative robotic systems would be proposed in future. Additionally, some advanced technology in other areas would also be applied into the system. In all, the system would become more intelligent, collaborative and autonomous.

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Safeguarding Collaborative Mobile Manipulators - Evaluation of the VALERI Workspace Monitoring System

Cited by 13 publications

References 1 publication

Human-Robot Perception in Industrial Environments: A Survey

Human-Robot Perception in Industrial Environments: A Survey

A Visuo-Haptic Guidance Interface for Mobile Collaborative Robotic Assistant (MOCA)

Collaborative mobile industrial manipulator: A review of system architecture and applications

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