Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model

Aolei Yang; Quan Liu; Wasif Naeem; Minrui Fei

doi:10.1007/978-981-16-7213-2_36

Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model

Aolei Yang, Quan Liu, Wasif Naeem^*, Minrui Fei

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents an unsupervised dynamic collision detection approach based on obstacle point cloud envelope model. Based on the generated robot envelope model using the D-H parameters, an invalid point cloud filtering method is designed to filter the point cloud data out of the reachable space of the robot. The oriented bounding box is then adopted to generate an approximate obstacle model. The relevant simplex is further calculated by fusing the robot envelope model, and the Gilbert-Johnson-Keerthi algorithm is employed to take the collision detection between model cells. A collision detection experimental platform was finally constructed to verify the feasibility and effectiveness of the proposed approach.

Original language	English
Title of host publication	Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings
Editors	Qinglong Han, Sean McLoone, Chen Peng, Baolin Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	370-378
Number of pages	9
ISBN (Print)	9789811672125
DOIs	https://doi.org/10.1007/978-981-16-7213-2_36
Publication status	Published - 23 Oct 2021
Event	7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021 - Hangzhou, China Duration: 22 Oct 2021 → 24 Oct 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1469 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021
Country/Territory	China
City	Hangzhou
Period	22/10/2021 → 24/10/2021

Bibliographical note

Funding Information:
This research was supported by Natural Science Foundation of Shanghai (18ZR1415100).

Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.

Keywords

Clustering segmentation
Collision detection
Obstacle model approximation
Robot envelope

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-16-7213-2_36Licence: Unspecified

Cite this

Yang, A., Liu, Q., Naeem, W., & Fei, M. (2021). Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model. In Q. Han, S. McLoone, C. Peng, & B. Zhang (Eds.), Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings (pp. 370-378). (Communications in Computer and Information Science; Vol. 1469 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7213-2_36

Yang, Aolei ; Liu, Quan ; Naeem, Wasif et al. / Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model. Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings. editor / Qinglong Han ; Sean McLoone ; Chen Peng ; Baolin Zhang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 370-378 (Communications in Computer and Information Science).

@inproceedings{89a5193c243e48c5b057a3c1b54d6819,

title = "Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model",

abstract = "This paper presents an unsupervised dynamic collision detection approach based on obstacle point cloud envelope model. Based on the generated robot envelope model using the D-H parameters, an invalid point cloud filtering method is designed to filter the point cloud data out of the reachable space of the robot. The oriented bounding box is then adopted to generate an approximate obstacle model. The relevant simplex is further calculated by fusing the robot envelope model, and the Gilbert-Johnson-Keerthi algorithm is employed to take the collision detection between model cells. A collision detection experimental platform was finally constructed to verify the feasibility and effectiveness of the proposed approach.",

keywords = "Clustering segmentation, Collision detection, Obstacle model approximation, Robot envelope",

author = "Aolei Yang and Quan Liu and Wasif Naeem and Minrui Fei",

note = "Funding Information: This research was supported by Natural Science Foundation of Shanghai (18ZR1415100). Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021 ; Conference date: 22-10-2021 Through 24-10-2021",

year = "2021",

month = oct,

day = "23",

doi = "10.1007/978-981-16-7213-2_36",

language = "English",

isbn = "9789811672125",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "370--378",

editor = "Qinglong Han and Sean McLoone and Chen Peng and Baolin Zhang",

booktitle = "Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings",

address = "Germany",

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Yang, A, Liu, Q, Naeem, W & Fei, M 2021, Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model. in Q Han, S McLoone, C Peng & B Zhang (eds), Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings. Communications in Computer and Information Science, vol. 1469 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 370-378, 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Hangzhou, China, 22/10/2021. https://doi.org/10.1007/978-981-16-7213-2_36

Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model. / Yang, Aolei; Liu, Quan; Naeem, Wasif et al.
Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings. ed. / Qinglong Han; Sean McLoone; Chen Peng; Baolin Zhang. Springer Science and Business Media Deutschland GmbH, 2021. p. 370-378 (Communications in Computer and Information Science; Vol. 1469 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model

AU - Yang, Aolei

AU - Liu, Quan

AU - Naeem, Wasif

AU - Fei, Minrui

PY - 2021/10/23

Y1 - 2021/10/23

N2 - This paper presents an unsupervised dynamic collision detection approach based on obstacle point cloud envelope model. Based on the generated robot envelope model using the D-H parameters, an invalid point cloud filtering method is designed to filter the point cloud data out of the reachable space of the robot. The oriented bounding box is then adopted to generate an approximate obstacle model. The relevant simplex is further calculated by fusing the robot envelope model, and the Gilbert-Johnson-Keerthi algorithm is employed to take the collision detection between model cells. A collision detection experimental platform was finally constructed to verify the feasibility and effectiveness of the proposed approach.

AB - This paper presents an unsupervised dynamic collision detection approach based on obstacle point cloud envelope model. Based on the generated robot envelope model using the D-H parameters, an invalid point cloud filtering method is designed to filter the point cloud data out of the reachable space of the robot. The oriented bounding box is then adopted to generate an approximate obstacle model. The relevant simplex is further calculated by fusing the robot envelope model, and the Gilbert-Johnson-Keerthi algorithm is employed to take the collision detection between model cells. A collision detection experimental platform was finally constructed to verify the feasibility and effectiveness of the proposed approach.

KW - Clustering segmentation

KW - Collision detection

KW - Obstacle model approximation

KW - Robot envelope

U2 - 10.1007/978-981-16-7213-2_36

DO - 10.1007/978-981-16-7213-2_36

M3 - Conference contribution

AN - SCOPUS:85118971428

SN - 9789811672125

T3 - Communications in Computer and Information Science

SP - 370

EP - 378

BT - Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings

A2 - Han, Qinglong

A2 - McLoone, Sean

A2 - Peng, Chen

A2 - Zhang, Baolin

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

Y2 - 22 October 2021 through 24 October 2021

ER -

Yang A, Liu Q, Naeem W, Fei M. Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model. In Han Q, McLoone S, Peng C, Zhang B, editors, Intelligent Equipment, Robots, and Vehicles - 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 370-378. (Communications in Computer and Information Science). doi: 10.1007/978-981-16-7213-2_36

Robot Dynamic Collision Detection Method Based on Obstacle Point Cloud Envelope Model

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this