首页|Study Data from Southern University of Science and Technology (SUSTech) Update Knowledge of Robotics (A Contact Parameter Estimation Method for Multi-modal Robot Locomotion On Deformable Granular Terrains)
Study Data from Southern University of Science and Technology (SUSTech) Update Knowledge of Robotics (A Contact Parameter Estimation Method for Multi-modal Robot Locomotion On Deformable Granular Terrains)
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NETL
NSTL
Cambridge Univ Press
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Robotics is now available. According to news reporting originating in Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “In this paper, we consider the problem of contact parameters (slippage and sinkage) estimation for multi-modal robot locomotion on granular terrains. To describe the contact events in the same framework for robots operated at different modes (e.g., wheel, leg), we propose a unified description of contact parameters for multi-modal robots.” Funders for this research include Science, Technology and Innovation Commission of Shenzhen Munic- ipality, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Southern University of Science and Technology (SUSTech), “We also provide a parameter estimation method for multi-modal robots based on CNN and DWT (discrete wavelet transformation) techniques and verify its effectiveness over different types of granular terrains. Besides motion modes, this paper also considers the influence of slope angles and the robot’s handing angles over contact parameters. Through comparison and analysis of the prediction results, our method can not only effectively predict the contact parameters of multi-modal robot locomotion on a granular medium (better than $96\%$ accuracy) but also achieves the same or better performance when compared to other (direct) contact measurement methods designed for individual motion modes, that is, single-modal robots such as quadruped robots and mars rovers.”
GuangdongPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningNano-robotRobotRoboticsSouthern University of Science and Technology (SUSTech)
NETL
NSTL
Cambridge Univ Press
