HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning

Changcong Wang; Chongyi Wei; Shaoping Bai; Yibin Li; Xincheng Tian; Lelai Zhou

doi:10.1109/RCAR58764.2023.10249917

HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning

Changcong Wang^*, Chongyi Wei, Shaoping Bai, Yibin Li, Xincheng Tian, Lelai Zhou

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

Human-Robot Collaboration (HRC) is people and robots work together in the same workspace to achieve shared goals. To ensure safety, robots need to perceive the position of the human body for planning purposes. However, there is no universal method for converting the human body into collision objects. To solve this problem, we propose an interface to convert the human body segments into collision objects in the robot planning scene. The interface estimates the human body Skinned Multi-Person Linear model (SMPL) by RGB image and measures the collision object size of the corresponding body segment. The corresponding size of collision objects are added to the corresponding positions of the human pose estimated by different types of sensors. The accuracy of human size estimation is verified on TotalMocap of AMASS. The interface with OctoMap is compared to evaluate the efficiency and the validity on interface is verified on the robot UR5e.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023
Number of pages	6
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	2023
Pages	423-428
Article number	10249917
ISBN (Print)	979-8-3503-2719-9
ISBN (Electronic)	979-8-3503-2718-2
DOIs	https://doi.org/10.1109/RCAR58764.2023.10249917
Publication status	Published - 2023
Event	2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023 - Datong, China Duration: 17 Jul 2023 → 20 Jul 2023

Conference

Conference	2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023
Country/Territory	China
City	Datong
Period	17/07/2023 → 20/07/2023
Sponsor	Beijing NOKOV Science and Technology Co., Cyborg and Bionic Systems, Galleon (Shanghai) Consulting Co., Ltd., Shanghai Society of Aeronautics

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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10.1109/RCAR58764.2023.10249917

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Wang, C., Wei, C., Bai, S., Li, Y., Tian, X., & Zhou, L. (2023). HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning. In Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023 (pp. 423-428). Article 10249917 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/RCAR58764.2023.10249917

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title = "HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning",

abstract = "Human-Robot Collaboration (HRC) is people and robots work together in the same workspace to achieve shared goals. To ensure safety, robots need to perceive the position of the human body for planning purposes. However, there is no universal method for converting the human body into collision objects. To solve this problem, we propose an interface to convert the human body segments into collision objects in the robot planning scene. The interface estimates the human body Skinned Multi-Person Linear model (SMPL) by RGB image and measures the collision object size of the corresponding body segment. The corresponding size of collision objects are added to the corresponding positions of the human pose estimated by different types of sensors. The accuracy of human size estimation is verified on TotalMocap of AMASS. The interface with OctoMap is compared to evaluate the efficiency and the validity on interface is verified on the robot UR5e.",

author = "Changcong Wang and Chongyi Wei and Shaoping Bai and Yibin Li and Xincheng Tian and Lelai Zhou",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023 ; Conference date: 17-07-2023 Through 20-07-2023",

year = "2023",

doi = "10.1109/RCAR58764.2023.10249917",

language = "English",

isbn = "979-8-3503-2719-9",

pages = "423--428",

booktitle = "Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

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Wang, C, Wei, C, Bai, S, Li, Y, Tian, X & Zhou, L 2023, HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning. in Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023., 10249917, IEEE (Institute of Electrical and Electronics Engineers), pp. 423-428, 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023, Datong, China, 17/07/2023. https://doi.org/10.1109/RCAR58764.2023.10249917

HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning. / Wang, Changcong; Wei, Chongyi; Bai, Shaoping et al.
Proceedings of the 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023. IEEE (Institute of Electrical and Electronics Engineers), 2023. p. 423-428 10249917.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T1 - HCRI

T2 - 2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

AU - Wang, Changcong

AU - Wei, Chongyi

AU - Bai, Shaoping

AU - Li, Yibin

AU - Tian, Xincheng

AU - Zhou, Lelai

PY - 2023

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AB - Human-Robot Collaboration (HRC) is people and robots work together in the same workspace to achieve shared goals. To ensure safety, robots need to perceive the position of the human body for planning purposes. However, there is no universal method for converting the human body into collision objects. To solve this problem, we propose an interface to convert the human body segments into collision objects in the robot planning scene. The interface estimates the human body Skinned Multi-Person Linear model (SMPL) by RGB image and measures the collision object size of the corresponding body segment. The corresponding size of collision objects are added to the corresponding positions of the human pose estimated by different types of sensors. The accuracy of human size estimation is verified on TotalMocap of AMASS. The interface with OctoMap is compared to evaluate the efficiency and the validity on interface is verified on the robot UR5e.

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SN - 979-8-3503-2719-9

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ER -

HCRI: A ROS2 Human Collision Object Interface for Robotic Manipulation Planning

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