Ushbu maqolada biz dinamik muhitda ishlaydigan mobil robotlar uchun yo'lni optimal rejalashtirishga erishish uchun sun'iy neyron tarmoqlarni (ANN) yo'lni rejalashtirish texnikasi bilan birlashtirgan algoritmik asosni taklif qilamiz. Asosiy maqsad - to'siqlar va turli xil atrof-muhit sharoitlarini hisobga olgan holda, robotlarni dinamik bo'shliqlar bo'ylab samarali boshqarish. Matlab dasturining ROS uskunalar panelida simulyatsiyalar va real tajribalar orqali yondashuvimizning samaradorligini ko'rsatamiz. Bizning natijalarimiz samaradorlik va muvaffaqiyat darajasi bo'yicha mavjud yo'lni rejalashtirish algoritmlariga nisbatan ayrim ko‘rsatkichlari yuqori samaradorlikni ko'rsatadi.
Individualized teaching, tailored to the unique needs and abilities of each learner, has long been an educational ideal. With the advent of artificialintelligence (Al) technology, this goal is becoming more achievable than ever before. This article explores the role of Al in individualized teaching, focusing on its applications in adaptive learning, personalized content delivery, and assessment. It also examines the benefits, challenges, and future prospects of integrating Al into education.
In peacetime, non-firearms of the lower jaw are usually observed. They arise as a result of resection or disarticulation of the jaw (in connection with a benign or malignant tumor), its elongation in the elimination of underdevelopment, after suffering osteomyelitis or overly extensive and uneconomical sequestrectomy, after an accidental trauma, etc. [6,9]. Among the many reasons that lead to the occurrence of defects in this localization, oncological pathology takes the first place, with malignant tumors accounting for up to 16% of cases, benign ones - up to 42.9% [10, 11, 12]. Traumatic injuries, gunshot and non-gunshot wounds of the maxillofacial region, leading to defects of the lower jaw of various lengths, occur in 28.5% of cases [7].