Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation
نویسندگان
چکیده
Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, search-and-rescue robotics. On the other hand, artificial intelligence systems allowed for improvement and adaptation of capabilities these robots based on specific imitating natural behavior animals. The main contribution this work is a method adjust adaptive gait patterns overcome terrains using ARTU-R (A1 Rescue Task UPM Robot) robot central pattern generator (CPG), automatic identification terrain characterization its obstacles (number, size, position superability analysis) through convolutional neural networks regulation. To develop method, study dog was carried out, validation adjustment simulation model ROS-Gazebo subsequent transfer real robot. Outdoor tests were out evaluate validate efficiency proposed terms percentage success overcoming stretches well kinematic dynamic variables results show that has an over 93% (identification terrain, segmentation obstacle characterization) 91% terrains. This also compared against developments state-of-the-art benchmark models.
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ژورنال
عنوان ژورنال: Biomimetics
سال: 2023
ISSN: ['2313-7673']
DOI: https://doi.org/10.3390/biomimetics8030289