title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ñ Ð¾Ð´ÐºÐ¾Ð¹ ÑÐ¾Ð±Ð¾ÑÐ°-Ð³ÐµÐºÑÐ°Ð¿Ð¾Ð´Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð¹ Ðº Ð½ÐµÐ¸ÑÐ¿ÑÐ°Ð²Ð½Ð¾ÑÑÑÐ¼ Ð´Ð²Ð¸Ð¶Ð¸ÑÐµÐ»ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a gait control system hexapode robot adaptive to motor failures
Creators: name: ÐÐµÑÑÐ²Ð°, ÐÐ½Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ¾Ð±Ð¾Ñ-Ð³ÐµÐºÑÐ°Ð¿Ð¾Ð´
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
ÑÐ¿ÑÐ¸Ð»Ð¾Ð½-Ð¶Ð°Ð´Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
hexapode robot
algorithms of learning
algorithms of adaptation
reinforcement learning
epsilon-greedy algorithm

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°Ð·ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÑÑÑ ÑÐ¸ÑÑÐµÐ¼Ð° Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸ Ð»Ð¾ÐºÐ¾Ð¼Ð¾ÑÐ¸Ð¹ Ð´Ð»Ñ ÑÐ¾Ð±Ð¾ÑÐ°-Ð³ÐµÐºÑÐ°Ð¿Ð¾Ð´Ð°, Ð¿ÑÐµÐ´Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð½Ð°Ñ Ð´Ð»Ñ ÑÐ°Ð¼Ð¾ÑÑÐ¾ÑÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸ ÑÐ¾Ð±Ð¾ÑÐ¾Ð¼ Ð¿Ð¾ÑÐ¾Ð´ÐºÐ¸ Ð¿ÑÐ¸ Ð½ÐµÐ¸ÑÐ¿ÑÐ°Ð²Ð½Ð¾ÑÑÐ¸ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸Ð· Ð´Ð²Ð¸Ð¶Ð¸ÑÐµÐ»ÐµÐ¹. ÐÐ»Ñ ÑÑÐ¾Ð³Ð¾ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑÐ¸ÐµÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼. ÐÐ¾ Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð±Ð·Ð¾ÑÐ° Ð±ÑÐ» Ð²ÑÐ±ÑÐ°Ð½ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð¼ÐµÑÐ¾Ð´, Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¹ Ð´Ð»Ñ Ð¸Ð¼ÐµÑÑÐµÐ¹ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ¾Ð±Ð¾ÑÐ°. ÐÐ»Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð±ÑÐ»Ð° Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ¾Ð±Ð¾ÑÐ°-Ð³ÐµÐºÑÐ°Ð¿Ð¾Ð´Ð° Ð² ÑÑÐµÐ´Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ½ÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ° Ð¿Ð°ÑÑÐµÑÐ½Ð¾Ð². Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, ÐµÑ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¸ Ð¿ÑÐ¾ÑÐµÐ½ÑÐ° ÑÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸.Â  ÐÐ»Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð½ÑÐµ ÑÑÑÐ°ÑÐµÐ³Ð¸Ð¸. ÐÐ»Ñ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð±ÑÐ» ÑÐ°ÐºÐ¶Ðµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ ÑÐ¿ÑÐ¸Ð»Ð¾Ð½-Ð¶Ð°Ð´Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼.
description: In this work, we are developing a locomotion adaptation system for a hexapod robot, designed for the robot to independently adapt its gait if one of the moverâs malfunctions. To do this, the algorithms used to solve this problem based on reinforcement learning were analyzed. But based on the review, the method most suitable for the existing robot model was selected and implemented. For experiments, a hexapod robot model was set up in a development and simulation environment. A learning and adaptation algorithm has been developed based on a central pattern generator. Experiments were also conducted to evaluate this implementation, its speed and percentage of convergence.Â  To improve the algorithms performance, different strategies were tested. For comparison, an epsilon-greedy algorithm was also implemented.