KO‘P-TUZILMALI MUHITDA TOZALOVCHI ROBOT UCHUN DISTRIBUTIONAL REINFORCEMENT LEARNING ASOSIDAGI MARSHRUT REJALASHTIRISH STRATEGIYASI
Keywords:
Muhitda tozalovchi robot, Path – planning, Distributional reinforcement learning, DQN Network, Sub – reward mechanismAbstract
Ushbu maqola nomaʼlum muhitlarda harakatlanuvchi tozalovchi robot uchun yangi marshrut rejalashtirish strategiyasini taklif etadi. Taklif qilingan yondashuv Lightweight Learned Image Denoising with Instance Adaptation (LIDIA) va Deep Q-Network (DQN) texnologiyalarini yagona ramkaga (framework) integratsiyalash orqali amalga oshiriladi. Ushbu kombinatsiya robotlarga ko‘p obʼyektli hududlarda to‘qnashuvsiz harakatlanish imkonini beradi. Birinchi bosqichda, muhitning chuqurlik tasvirlari robotning o‘rnatilgan kamerasidan olinadi. Bu xom tasvirlar odatda shovqinli bo‘lgani sababli, LIDIA texnologiyasi qo‘llanilib, maʼlumotlar kalibrlanadi va tasvir sifati yaxshilanadi.
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