Terrain Identification for Humanoid Robots Applying Convolutional Neural Networks

dc.contributor.advisorRobot sensing systems
dc.contributor.advisorLegged locomotion
dc.contributor.advisorAcoustics
dc.contributor.authorVENANCIO, MURILO MENDONCA
dc.contributor.authorGONCALVES, ROGERIO SALES
dc.contributor.authorBianchi, Reinaldo
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2021-11-24T17:08:52Z
dc.date.available2021-11-24T17:08:52Z
dc.date.issued2020-06-05
dc.description.abstractStable and efficient walking strategies for humanoid robots usually relies on assumptions regarding terrain characteristics. If the robot is able to classify the ground type at the footstep moment, it is possible to take preventive actions to avoid falls and to reduce energy consumption. In this article, a new terrain identification method that makes use of a convolutional neural network to classify the terrain type is proposed. The input of this network is a bidimensional matrix-a signature image of the impact-composed of raw data from inertial and torque sensors, sampled after the impact between the foot and ground. Six different terrains types were selected for testing the proposed approach: these terrains were tested both in simulation using the Gazebo 9.0 environment and with one real robot, designed to satisfy the requirements for the RoboCup Humanoid Competition. In both robots the ROS framework was used, and the LIPM was the selected approach for the biped walking pattern generation. Results showed that the proposed method was able to classify the terrain type with an accuracy greater than 98% for both real and virtual situations, surpassing current state-of-the-art terrain classification methods for legged robots.
dc.description.firstpage1433
dc.description.issuenumber3
dc.description.lastpage1444
dc.description.volume26
dc.description.volume26
dc.identifier.citationVENANCIO, MURILO MENDONCA; GONCALVES, ROGERIO SALES; BIANCHI, REINALDO A C. Terrain Identification for Humanoid Robots Applying Convolutional Neural Networks. IEEE-ASME TRANSACTIONS ON MECHATRONICS, v. 26, n. 3, p. 1433-1444, 2020.
dc.identifier.doi10.1109/TMECH.2020.3020781
dc.identifier.issn1083-4435
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3473
dc.relation.ispartofIEEE-ASME TRANSACTIONS ON MECHATRONICS
dc.rightsAcesso Restrito
dc.titleTerrain Identification for Humanoid Robots Applying Convolutional Neural Networkspt_BR
dc.typeArtigopt_BR
fei.source.urlhttps://ieeexplore.ieee.org/document/9183954
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