Human-object integrated assembly intention recognition for context-aware human-robot collaborative assembly |
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| Affiliation: | 1. Institute of Smart Manufacturing Systems, Chang’an University, Xi’an, China;2. Key Laboratory of Road Construction Technology and Equipment, Chang’an University, Xi’an, China;3. School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, China;4. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;1. Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;2. Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;1. Center of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China;2. Key Lab of Ultra-precision Intelligent Instrumentation Engineering (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150080, China;3. Institute of Reactor Operation and Application, Nuclear Power Institute of China, Chengdu 610000, China;1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China;2. College of Mechanical Engineering, Donghua University, Shanghai 201620, China;3. Shanghai Waigaoqiao Shipbuilding Company, Shanghai 200137, China;1. Key Laboratory of Industrial Engineering and Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi''an, Shaanxi, PR China;2. College of Mechanical Engineering, Xi''an University of Science and Technology, Xi''an, Shaanxi, PR China;3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore;4. Department of Industrial and manufacturing systems Engineering, The University of Hong Kong, Hong Kong;1. Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an, Shaanxi 710064, China;2. Institute of Smart Manufacturing Systems Engineering, Chang’an University, Xi’an, Shaanxi 710064, China;3. State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China;4. Hubei University of Automotive Technology, Shiyan, Hubei 442002, China;5. Chengdu Research Institute, City University of Hong Kong, Chengdu 610200, China;6. Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China;1. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China;2. School of Mechanical and Electrical Engineering, and the National Demonstration Center for Experiment Electronic Circuit Education, Guilin University of Electronic Technology, Guilin 541004, China;3. Mechanical and Aerospace Engineering Department Monash University, Melbourne 3168, Australia |
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| Abstract: | Human-robot collaborative (HRC) assembly combines the advantages of robot's operation consistency with human's cognitive ability and adaptivity, which provides an efficient and flexible way for complex assembly tasks. In the process of HRC assembly, the robot needs to understand the operator's intention accurately to assist the collaborative assembly tasks. At present, operator intention recognition considering context information such as assembly objects in a complex environment remains challenging. In this paper, we propose a human-object integrated approach for context-aware assembly intention recognition in the HRC, which integrates the recognition of assembly actions and assembly parts to improve the accuracy of the operator's intention recognition. Specifically, considering the real-time requirements of HRC assembly, spatial-temporal graph convolutional networks (ST-GCN) model based on skeleton features is utilized to recognize the assembly action to reduce unnecessary redundant information. Considering the disorder and occlusion of assembly parts, an improved YOLOX model is proposed to improve the focusing capability of network structure on the assembly parts that are difficult to recognize. Afterwards, taking decelerator assembly tasks as an example, a rule-based reasoning method that contains the recognition information of assembly actions and assembly parts is designed to recognize the current assembly intention. Finally, the feasibility and effectiveness of the proposed approach for recognizing human intentions are verified. The integration of assembly action recognition and assembly part recognition can facilitate the accurate operator's intention recognition in the complex and flexible HRC assembly environment. |
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| Keywords: | Human-robot collaborative assembly Human intention recognition ST-GCN Part recognition Improved YOLOX |
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