Bagheri Kudakani, S., Lenjannejadian, S., Poursina, M. (2013). Optimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 6(2), 11-20.
S. Bagheri Kudakani; S. Lenjannejadian; M. Poursina. "Optimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump". Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 6, 2, 2013, 11-20.
Bagheri Kudakani, S., Lenjannejadian, S., Poursina, M. (2013). 'Optimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump', Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 6(2), pp. 11-20.
Bagheri Kudakani, S., Lenjannejadian, S., Poursina, M. Optimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 2013; 6(2): 11-20.
Optimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump
In human body movement simulation such as vertical jump by a forward dynamic model, optimal control theories must be used. In the recent years, new methods were created for solving optimization problems which they were adopted from animal behaviors and environment events such as Genetic algorithm, Particle swarm and Imperialism competitive. In this work, the skeletal model was constructed by Newton-Euler equation of motion. This 2D model has 4 rigid segments that include foot, shank, thigh and HAT (Head, Arm and Trunk) and all joints were assumed to be revolute and ideal. Also 20 effective muscles in vertical jump were constructed as joint actuator. The ground reaction force was simulated by a spring-damper element. Additionally, joints ligament were constructed to simulate the joint out of range motion. The Genetic algorithm was used to generate the best muscle excitation for maximum height in vertical jumping and the generated muscles excitations were converted to muscles activations. The muscles activations were applied to muscles model to generate muscles force. The maximum height of jump was considered as a criteria function of optimization problem. The designed genetic algorithm could control the musculoskeletal and simulate the vertical jump movement. The result showed that the height of center of mass was equal to 121.67 cm after 533 iterations. It is looks to be able to obtain better result provided to increase the iteration or combining clever algorithms together.
برای شبیه سازی حرکتهای بدن همانند پرش عمودی با استفاده از مدلهای دینامیک مستقیم، میتوان از تئوری‌های کنترل بهینه استفاده کرد. در سالهای اخیر روش‌های مدرن همانند ازدحام ذرات، رقابت استعماری و الگوریتم ژنتیک برای حل مسائل بهینهسازی مطرح شده است که از رفتارهای حیوانات و پدیدههای طبیعی الهام گرفته شدهاند. در این تحقیق برای شبیه سازی پرش عمودی از یک مدل چهار لینکی صلب که شامل پا، ساق، ران و بخش چهارم شامل تنه، گردن و دستان می‌باشد، استفاده شد و تمامی مفاصل آن نیز لولایی و ایده‌آل در نظر گرفته شد. برای به حرکت در آوردن مدل و ایجاد گشتاور در مفاصل، بیست عضله موثر در حرکت پرش عمودی شبیه سازی شدند. همچنین برای شبیه سازی نیروی عکس العمل زمین از یک المان دمپر- فنر در کف پا استفاده شد و برای جلوگیری از حرکت مفاصل خارج از دامنه حرکتی، لیگامنتها نیز در مفاصل لحاظ شدند. از الگوریتم ژنتیک نیز برای تولید بهترین تحریک عصبی در جهت دستیابی به حداکثر ارتفاع پرش استفاده شد و تحریکات عضلانی تولید شده به ضریب فعالسازی عضلات تبدیل گردید. ضرایب بدست آمده نیز برای تولید نیرو به مدل عضلات اعمال شدند. همچنین تابع معیار حداکثر ارتفاع پرش در نظر گرفته شد. نتایج نشان داد که مرکز جرم مدل پس از 533 تکرار به ارتفاع 67/121 سانتیمتری رسیده است. البته به نظر میرسد با بهبود پارامترهای این الگوریتم و ترکیب الگوریتمهای هوشمند بتوان به الگویی کاملتر دست یافت.
Keywords [Persian]
مدل اسکلتی عضلانی، کنترل بهینه، پرش عمودی، الگوریتم ژنتیک
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