Speed-invariant terrain roughness classification and control based on inertial sensors

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dc.creatorFelipe G. Oliveira
dc.creatorElerson R. S. Santos
dc.creatorArmando Alves Neto
dc.creatorMário Fernando Montenegro Campos
dc.creatorDouglas G. Macharet
dc.date.accessioned2025-03-26T16:22:40Z
dc.date.accessioned2025-09-08T22:55:25Z
dc.date.available2025-03-26T16:22:40Z
dc.date.issued2017
dc.identifier.doi10.1109/SBR-LARS-R.2017.8215332
dc.identifier.urihttps://hdl.handle.net/1843/80964
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofIEEE Latin American Robotics Symposium - LARS
dc.rightsAcesso Restrito
dc.subjectRobôs - Sistemas de controle
dc.subjectRobótica
dc.subjectVisão por computador
dc.subject.otherNavigation , Cameras , Robot vision systems , Vibrations
dc.subject.otherInertial Measurement Unit , Terrain Roughness , Autonomous Vehicles , Real-world Experiments , Mining Industry , Outdoor Applications , Safe Navigation , Efficient Navigation , Training Data , Training Dataset , Low-pass , Optimal Control , Recurrent Neural Network , Lookup Table , Path Planning , Visual Conditions , Speed Control , Low Roughness , Motion Velocity , Robot Navigation , Inertial Data , Vibration Data , Uneven Terrain , Roughness Level , Forward Speed , Rugged Terrain , Measurement Window , Velocity Of The Robot , Accelerometer , Navigation Performance
dc.titleSpeed-invariant terrain roughness classification and control based on inertial sensors
dc.typeArtigo de evento
local.citation.spage1
local.description.resumoNowadays, it is notorious the increasing use of autonomous vehicles in different outdoor field applications such as agriculture, the mining industry, environmental monitoring, among others. In this context, the determination of the traversability level of a terrain is a fundamental task for safe and efficient navigation of Autonomous Ground Vehicles (AGVs) in unstructured unknown outdoor environments. Information like roughness of the ground is important when velocity control and other dynamic issues are concerned. However, most of the techniques in the literature use camera or lidar sensors to evaluate the ground around the robot, leading to complex and high cost systems. More simpler methods use only inertial sensors to estimate the roughness properties of the terrain, however they can be very sensitive to the robot's speed. In this paper, we propose a novel classifier capable of cluster different terrains based only on acceleration data provided by an Inertial Measurement Unit (IMU). We demonstrate with real-world experiments that, for different forward velocities, the mean accuracy of the classification exceeds 80 %. Our method also incorporates a controller to regulate the speed according to the terrain identified by the robot in order to avoid abrupt movements over terrain changes.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8215332

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