{"title":"面向人机协作的安全多通道通信","authors":"Gorkem Anil Al, Uriel Martinez-Hernandez","doi":"10.1016/j.rcim.2025.103109","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a safe multi-channel communication and safety system for human–robot collaboration (HRC) in industrial applications enabled by the DiGeTac unit. This unit integrates gesture, distance, and custom-designed tactile sensors, with gesture and distance elements on the top and the tactile element on the bottom. This design provides enhanced multimodal safety and interaction, enabling both close proximity and long-distance perception, making the DiGeTac unit highly suitable for various collaborative scenarios. Unlike other multimodal sensors, DiGeTac offers contactless and touch-based interaction, and post- and pre-collision safety features for a broader range of tasks in HRC environments. The performance of each sensing element within the DiGeTac unit is thoroughly evaluated through a series of validation experiments with a robot arm. The distance sensor’s accuracy is assessed in pre-collision scenarios, ensuring reliable proximity detection for collision avoidance as part of the safety strategy. The tactile sensor is tested in a post-collision scenario, where it functions as a safety mechanism to detect impacts and trigger protective responses. The capability of hand gestures recognition to facilitate intuitive human–robot communication is evaluated using an artificial neural network (ANN). Additionally, the tactile sensor’s contact estimation is analysed with a convolutional neural network (CNN), enhancing the robot’s ability to interact with humans and perform collaborative tasks. Finally, both safety and interaction strategies are tested in HRC scenarios, where the human operator commands the robot to move to specific positions. The results show that the DiGeTac unit is effective and has potential to improve complex collaborative tasks.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103109"},"PeriodicalIF":11.4000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Safe multi-channel communication for human–robot collaboration\",\"authors\":\"Gorkem Anil Al, Uriel Martinez-Hernandez\",\"doi\":\"10.1016/j.rcim.2025.103109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a safe multi-channel communication and safety system for human–robot collaboration (HRC) in industrial applications enabled by the DiGeTac unit. This unit integrates gesture, distance, and custom-designed tactile sensors, with gesture and distance elements on the top and the tactile element on the bottom. This design provides enhanced multimodal safety and interaction, enabling both close proximity and long-distance perception, making the DiGeTac unit highly suitable for various collaborative scenarios. Unlike other multimodal sensors, DiGeTac offers contactless and touch-based interaction, and post- and pre-collision safety features for a broader range of tasks in HRC environments. The performance of each sensing element within the DiGeTac unit is thoroughly evaluated through a series of validation experiments with a robot arm. The distance sensor’s accuracy is assessed in pre-collision scenarios, ensuring reliable proximity detection for collision avoidance as part of the safety strategy. The tactile sensor is tested in a post-collision scenario, where it functions as a safety mechanism to detect impacts and trigger protective responses. The capability of hand gestures recognition to facilitate intuitive human–robot communication is evaluated using an artificial neural network (ANN). Additionally, the tactile sensor’s contact estimation is analysed with a convolutional neural network (CNN), enhancing the robot’s ability to interact with humans and perform collaborative tasks. Finally, both safety and interaction strategies are tested in HRC scenarios, where the human operator commands the robot to move to specific positions. The results show that the DiGeTac unit is effective and has potential to improve complex collaborative tasks.</div></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":\"97 \",\"pages\":\"Article 103109\"},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736584525001632\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584525001632","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Safe multi-channel communication for human–robot collaboration
This paper presents a safe multi-channel communication and safety system for human–robot collaboration (HRC) in industrial applications enabled by the DiGeTac unit. This unit integrates gesture, distance, and custom-designed tactile sensors, with gesture and distance elements on the top and the tactile element on the bottom. This design provides enhanced multimodal safety and interaction, enabling both close proximity and long-distance perception, making the DiGeTac unit highly suitable for various collaborative scenarios. Unlike other multimodal sensors, DiGeTac offers contactless and touch-based interaction, and post- and pre-collision safety features for a broader range of tasks in HRC environments. The performance of each sensing element within the DiGeTac unit is thoroughly evaluated through a series of validation experiments with a robot arm. The distance sensor’s accuracy is assessed in pre-collision scenarios, ensuring reliable proximity detection for collision avoidance as part of the safety strategy. The tactile sensor is tested in a post-collision scenario, where it functions as a safety mechanism to detect impacts and trigger protective responses. The capability of hand gestures recognition to facilitate intuitive human–robot communication is evaluated using an artificial neural network (ANN). Additionally, the tactile sensor’s contact estimation is analysed with a convolutional neural network (CNN), enhancing the robot’s ability to interact with humans and perform collaborative tasks. Finally, both safety and interaction strategies are tested in HRC scenarios, where the human operator commands the robot to move to specific positions. The results show that the DiGeTac unit is effective and has potential to improve complex collaborative tasks.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.