首页|Reports from Aston University Describe Recent Advances in Robotics (An Ontology and Rule-based Method for Human-robot Collaborative Disassembly Planning In Smar t Remanufacturing)
Reports from Aston University Describe Recent Advances in Robotics (An Ontology and Rule-based Method for Human-robot Collaborative Disassembly Planning In Smar t Remanufacturing)
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Current study results on Robotics have been published. According to news reporting from Birmingham, United Kingdom, by NewsRx journalists, research stated, "Disassembly is a decisive step in the rem anufacturing process of End -of -Life (EoL) products. As an emerging semi -autom atic disassembly paradigm, human-robot collaborative disassembly (HRCD) offers m ultiple disassembly methods to enhance flexibility and efficiency." Funders for this research include RECLAIM project ‘Remanufacturing and Refurbish ment of Large Industrial Equipment', Horizon 2020, National Natural Science Foun dation of China (NSFC). The news correspondents obtained a quote from the research from Aston University , "However, HRCD increases the complexity of planning and determining the optima l disassembly sequence and scheme. Currently, the optimisation process of heuris tic methods is difficult to interpret, and the results cannot be guaranteed as g lobally optimal. Consequently, this paper introduces a general ontology model fo r HRCD, along with a rule -based reasoning method, to automatically generate the optimal disassembly sequence and scheme. Firstly, the HRCD ontology model estab lishes the disassembly -related information for EoL products in a standardised a pproach. Then, customised disassembly -related rules are proposed to regulate th e precedence constraints and optional disassembly methods for each disassembly t ask of EoL products. The optimal disassembly sequence and scheme are automatical ly generated by combining supportive rules with the ontology model. Lastly, the human-robot collaborative disassembly planning of a gearbox is presented as a ca se study to validate the feasibility of the proposed methods. Our method generat es an optimal disassembly scheme compared with other heuristic algorithms, achie ving the shortest process time of 308 units and the fewest number of disassembly direction change of 3 times. Additionally, the reasoning procedure can be easil y tracked and modified."
BirminghamUnited KingdomEuropeEmer ging TechnologiesMachine LearningRobotRoboticsAston University
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