Liana Bertoni;Lorenzo Baccelliere;Luca Muratore;Nikos G. Tsagarakis
{"title":"A Proximity-Based Framework for Human-Robot Seamless Close Interactions","authors":"Liana Bertoni;Lorenzo Baccelliere;Luca Muratore;Nikos G. Tsagarakis","doi":"10.1109/LRA.2025.3585762","DOIUrl":null,"url":null,"abstract":"The administration and monitoring of shared workspaces are crucial for seamlessly integrating robots to operate in close interactions with humans. Adaptive, versatile, and reliable robot movements are key to achieving effective and successful human-robot synergy. In situations involving unexpected or unintended collisions, robots must react appropriately to minimize risks to humans while still staying focused on their primary tasks or safely resuming them. Although collision detection and identification algorithms are well-established, more advanced robot reactions beyond basic stop-and-wait reactions have not yet been widely adopted and understood. This limitation highlights the need for more sophisticated robot responses to better handle complex collision scenarios, ensuring both safety and task continuity. This letter introduces a novel complete robotic system that leverages the potential of on-board proximity sensor equipment to seamlessly furnish compatible robot reactions while operating in close interactions. With on-board distributed proximity sensors, the robot gains a continuous close workspace awareness, facilitating a transparent negotiation of potential collisions while executing tasks. The proposed system and framework are validated in a collaborative industrial task scenario composed of sub-tasks allocated to the human and the robot and performed within shared regions of the workspace, demonstrating the efficacy of the approach.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 8","pages":"8514-8521"},"PeriodicalIF":5.3000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11067953/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The administration and monitoring of shared workspaces are crucial for seamlessly integrating robots to operate in close interactions with humans. Adaptive, versatile, and reliable robot movements are key to achieving effective and successful human-robot synergy. In situations involving unexpected or unintended collisions, robots must react appropriately to minimize risks to humans while still staying focused on their primary tasks or safely resuming them. Although collision detection and identification algorithms are well-established, more advanced robot reactions beyond basic stop-and-wait reactions have not yet been widely adopted and understood. This limitation highlights the need for more sophisticated robot responses to better handle complex collision scenarios, ensuring both safety and task continuity. This letter introduces a novel complete robotic system that leverages the potential of on-board proximity sensor equipment to seamlessly furnish compatible robot reactions while operating in close interactions. With on-board distributed proximity sensors, the robot gains a continuous close workspace awareness, facilitating a transparent negotiation of potential collisions while executing tasks. The proposed system and framework are validated in a collaborative industrial task scenario composed of sub-tasks allocated to the human and the robot and performed within shared regions of the workspace, demonstrating the efficacy of the approach.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.