Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A challenge for automating mechanical assembly is that cumulative uncertainties typically exceed part clearances, which makes conventional position-based tactics unsuccessful. Force-based assembly strategies offer a potential solution, although such methods are still poorly understood and can be difficult to program. ,  describe a force-based robotic assembly approach that uses fixed strategies with tunable parameters. A generic assembly strategy suitable for execution on an industrial robot is selected by the programmer. Parameters are then self-tuned empirically by the robot using a genetic-algorithm learning process that seeks to minimize assembly time subject to contact-force limits. Results are presented for two automotive part assembly examples using ABB robots with commercial force-control software, showing that the approach is highly effective and suitable for industrial use.
['Jeremy A. Marvel', 'Wyatt S. Newman', 'Dave Gravel', 'George Zhang', 'Jianjun Wang', 'Thomas A. Fuhlbrigge']
I am writing a research paper on a idea Automated learning for parameter optimization of robotic assembly tasks utilizing genetic algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Automated learning for parameter optimization of robotic assembly tasks utilizing genetic algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A challenge for automating mechanical assembly is that cumulative uncertainties typically exceed part clearances, which makes conventional position-based tactics unsuccessful. Force-based assembly strategies offer a potential solution, although such methods are still poorly understood and can be difficult to program. ,  describe a force-based robotic assembly approach that uses fixed strategies with tunable parameters. A generic assembly strategy suitable for execution on an industrial robot is selected by the programmer. Parameters are then self-tuned empirically by the robot using a genetic-algorithm learning process that seeks to minimize assembly time subject to contact-force limits. Results are presented for two automotive part assembly examples using ABB robots with commercial force-control software, showing that the approach is highly effective and suitable for industrial use."}]