In industrial scenarios, requiring human–robot collaboration, the understanding between the human operator and his/her robot coworker is paramount. On the one side, the robot has to detect human intentions, and on the other side, the human needs to be aware of what is happening during the collaborative task. In this letter, we address the first issue by predicting human behavior through a new recursive Bayesian classifier, exploiting head, and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgments to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human–robot collaboration scenario, and we found that adding haptic feedback is particularly helpful to improve the performance when the human–robot cooperation task is performed by nonskilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interface, able to give information about the prediction reliability, are essential features to improve performance in a human–robot collaboration in industrial environments.
Casalino, A., Messeri, C., Pozzi, M., Zanchettin, A.M., Rocco, P., & Prattichizzo, D. (2018). Operator awareness in human–robot collaboration through wearable vibrotactile feedback. IEEE ROBOTICS AND AUTOMATION LETTERS, 3(4), 4289-4296 [10.1109/LRA.2018.2865034].
|Titolo:||Operator awareness in human–robot collaboration through wearable vibrotactile feedback|
|Citazione:||Casalino, A., Messeri, C., Pozzi, M., Zanchettin, A.M., Rocco, P., & Prattichizzo, D. (2018). Operator awareness in human–robot collaboration through wearable vibrotactile feedback. IEEE ROBOTICS AND AUTOMATION LETTERS, 3(4), 4289-4296 [10.1109/LRA.2018.2865034].|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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|RAL_18.pdf||Accepted version. European Union (EU), Horizon 2020 programme. European project “Synergy-based Open-source Foundations and Technologies for Prosthetics and RehabilitatiOn” (SoftPro), Research & Innovation Action, Grant Agreement n. 688857. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Digital Object Identifier (DOI): 10.1109/LRA.2018.2865034||PDF editoriale||PUBBLICO - Pubblico con Copyright||Open Access Visualizza/Apri|