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Haptic-Aided Navigation Vehicle:Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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摘要 As autonomous mobile robots are increasingly deployed in complex environments,traditional vision sensors and LiDAR encounter considerable limitations,particularly in detecting obstacles in blind spots or transparent objects.To address the issue of blind spots,we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle(HANV).This system integrates haptic sensors and LiDAR to deliver comprehensive perception,significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors.To tackle the challenge of transparent obstacles,which are often undetected by both vision and LiDAR sensors,we employ a fusion of haptic sensors and LiDAR.The haptic system provides physical contact feedback,ensuring reliable detection of transparent obstacles such as glass,while LiDAR offers long-range sensing capabilities.This combination enables HANV to navigate effectively in environments with transparent obstacles,overcoming the limitations of traditional sensing systems.Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles.HANV achieves high success rates,minimal collisions,and efficient obstacle avoidance,particularly excelling in complex,confined spaces where conventional systems prove inadequate.These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments.
作者 LI Mingwang LI Xinde ZHANG Zhentong ZHANG Zeyu ZHAO Haoming 李名旺;李新德;张朕通;张泽宇;赵浩鸣
机构地区 School of Software
出处 《Journal of Shanghai Jiaotong university(Science)》 2026年第1期167-175,共9页 上海交通大学学报(英文版)
基金 the National Natural Science Foundation of China(Nos.62233003 and 62073072) the Key R&D Program of Jiangsu Province(Nos.BE2020006 and BE2020006-1) the Shenzhen Science and Technology Program(Nos.JCYJ20210324132202005 and JCYJ20220818101206014)。

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