{"title":"A Spatio-Temporal Prediction and Planning Framework for Proactive Human-Robot Collaboration","authors":"Jared Flowers, Gloria Wiens","doi":"10.1115/1.4063502","DOIUrl":null,"url":null,"abstract":"Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063502","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining