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However, existing interfaces for human-robot interaction / cooperation (HRI/C) have limited levels of intuitiveness to use and safety is a major concern when humans and robots share the same workspace. Many times, this is due to the lack of a reliable estimation of the human pose in space which is the primary input to calculate the human-robot minimum distance (required for safety and collision avoidance) and HRI/C featuring machine learning algorithms classifying human behaviours / gestures. Each sensor type has its own characteristics resulting in problems such as occlusions (vision) and drift (inertial) when used in an isolated fashion. In this paper, it is proposed a combined system that merges the human tracking provided by a 3D vision sensor with the pose estimation provided by a set of inertial measurement units (IMUs) placed in human body limbs. The IMUs compensate the gaps in occluded areas to have tracking continuity. To mitigate the lingering effects of the IMU offset we propose a continuous online calculation of the offset value. Experimental tests were designed to simulate human motion in a human-robot collaborative environment where the robot moves away to avoid unexpected collisions with de human. 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Assembly processes are required to adapt in order to meet the requirements of increased product complexity and constant variant updates. A concept to meet challenges within this trend, is a close collaboration between human workers and robots. Currently, in order to protect human operators, there are barriers and restrictions in place which prevent close collaboration. This is due to safety systems being mostly reactive, rather than anticipating motions or intentions. There are probabilistic models, which aim to overcome these limitations, yet predicting human behavior remains highly complex. Thus, it would be desirable to physically measure movement intentions in advance. A novel approach is presented of how upper-limb movement intentions can be measured with a mobile electroencephalogram (EEG). The human brain constantly analyses and evaluates motor movements up to 0.5 s before their execution. A safety system could therefore be enhanced to have an early warning of an upcoming movement. In order to classify the EEG-signals as fast as possible and to minimize fine-tuning efforts, a novel data processing methodology is introduced. This includes TimeSeriesKMeans labelling of movement intentions, which is then used to train a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The results suggested high detection accuracies and potential time gains of up to 513 ms to be achieved in a semi-online system. Thus, the time advantages included in a simulation demonstrated the potential to increase a system's reaction time and therefore improve the safety and the fluency of Human-Robot Collaboration.", "publicationTitle": "Robotics and Computer-Integrated Manufacturing", "publisher": "", "place": "", "date": "August 1, 2021", "volume": "70", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "102137", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Robotics and Computer-Integrated Manufacturing", "DOI": "10.1016/j.rcim.2021.102137", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0736584521000223", "accessDate": "2021-04-12T19:40:45Z", "PMID": "", "PMCID": "", "ISSN": "0736-5845", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "EEG", "type": 1 }, { "tag": "Human-Robot Collaboration", "type": 1 }, { "tag": "Machine Learning", "type": 1 }, { "tag": "Movement intention recognition", "type": 1 }, { "tag": "Safety in robotics", "type": 1 } ], "collections": [ "KJTQ43A9" ], "relations": {}, "dateAdded": "2021-04-12T19:40:45Z", "dateModified": "2021-04-12T19:40:45Z" } }, { "key": "RNPVXL7A", "version": 255, "library": { "type": "group", "id": 236562, "name": "Interaction Spaces", "links": { "alternate": { "href": "https://www.zotero.org/groups/interaction_spaces", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/236562/items/RNPVXL7A", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/interaction_spaces/items/RNPVXL7A", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/236562/items/FTHLVEDY", "type": "application/json" } }, "meta": { "createdByUser": { "id": 132502, "username": "fkurfess", "name": "Franz J. 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Kurfess", "links": { "alternate": { "href": "https://www.zotero.org/fkurfess", "type": "text/html" } } }, "creatorSummary": "Liu and Wang", "parsedDate": "2021-02-01", "numChildren": 1 }, "data": { "key": "FTHLVEDY", "version": 255, "itemType": "journalArticle", "title": "Collision-free human-robot collaboration based on context awareness", "creators": [ { "creatorType": "author", "firstName": "Hongyi", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Lihui", "lastName": "Wang" } ], "abstractNote": "Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.", "publicationTitle": "Robotics and Computer-Integrated Manufacturing", "publisher": "", "place": "", "date": "February 1, 2021", "volume": "67", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "101997", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Robotics and Computer-Integrated Manufacturing", "DOI": "10.1016/j.rcim.2020.101997", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0736584520302088", "accessDate": "2021-04-12T19:38:11Z", "PMID": "", "PMCID": "", "ISSN": "0736-5845", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Collision-free system", "type": 1 }, { "tag": "Context awareness", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Human-robot collaboration", "type": 1 } ], "collections": [ "KJTQ43A9" ], "relations": {}, "dateAdded": "2021-04-12T19:38:21Z", "dateModified": "2021-04-12T19:38:21Z" } }, { "key": "XXCW6ARY", "version": 252, "library": { "type": "group", "id": 236562, "name": "Interaction Spaces", "links": { "alternate": { "href": "https://www.zotero.org/groups/interaction_spaces", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/236562/items/XXCW6ARY", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/interaction_spaces/items/XXCW6ARY", "type": "text/html" } }, "meta": { "createdByUser": { "id": 132502, "username": "fkurfess", "name": "Franz J. 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Written for students from diverse backgrounds, it presents relevant background concepts, describing how robots work, how to design them, and how to evaluate their performance. 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We begin by discussing the need for a toolkit of HRI metrics. We then describe the framework of our work and identify important biasing factors that must be taken into consideration. Finally, we present suggested common metrics for standardization and a case study. 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