Detection and Management of Human-Cable Collision in Cable-Driven Parallel Robots

This letter discusses the challenges and innovations in collision detection and management strategies for Cable-Driven Parallel Robots (CDPRs), focusing on enhancing safety in human-robot collaborative environments. A comprehensive collision management method is introduced. It integrates a novel method to detect collisions and identify the collided cable, leveraging tension sensor data and algorithmic strategies to improve accuracy and response to collision events. Adaptive management strategies for different collision severities, including minor and severe contacts, are presented, along with procedures for post-collision management. The methodologies are validated through experimentation with the CRAFT prototype, demonstrating their practical effectiveness. The findings have significant implications for the design and implementation of safety protocols in CDPRs.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-10-28
DOI
https://doi.org/10.1109/lra.2024.3487051
Citations
5
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
1.64

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article

Detection and Management of Human-Cable Collision in Cable-Driven Parallel Robots

H. Gao, Christine Chevallereau, Stéphane Caro
5 citations
IEEE Robotics and Automation Letters
Robot Manipulation and Learning
1.64
article

Detection and Management of Human-Cable Collision in Cable-Driven Parallel Robots

H. Gao, Christine Chevallereau, Stéphane Caro
article en
5 citations

Abstract

This letter discusses the challenges and innovations in collision detection and management strategies for Cable-Driven Parallel Robots (CDPRs), focusing on enhancing safety in human-robot collaborative environments. A comprehensive collision management method is introduced. It integrates a novel method to detect collisions and identify the collided cable, leveraging tension sensor data and algorithmic strategies to improve accuracy and response to collision events. Adaptive management strategies for different collision severities, including minor and severe contacts, are presented, along with procedures for post-collision management. The methodologies are validated through experimentation with the CRAFT prototype, demonstrating their practical effectiveness. The findings have significant implications for the design and implementation of safety protocols in CDPRs.

IEEE Robotics and Automation LettersVol. 9(12)
École Centrale de Nantes (FR), Centre National de la Recherche Scientifique (FR), Laboratoire des Sciences du Numérique de Nantes (FR), Nantes Université (FR)
Agence Nationale de la Recherche, China Scholarship Council
Openalex Percentile: Top 16%
Robot Manipulation and Learning
1.64
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