Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots
To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle featur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/5/311 |
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| author | Yuepeng Zhang Guangzhong Cao Jun Wu Bo Gao Linzhong Xia Chen Lu Hui Wang |
| author_facet | Yuepeng Zhang Guangzhong Cao Jun Wu Bo Gao Linzhong Xia Chen Lu Hui Wang |
| author_sort | Yuepeng Zhang |
| collection | DOAJ |
| description | To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions. |
| format | Article |
| id | doaj-art-e4d5c52511e4449e82dfd898201db383 |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-e4d5c52511e4449e82dfd898201db3832025-08-20T03:14:40ZengMDPI AGBiomimetics2313-76732025-05-0110531110.3390/biomimetics10050311Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton RobotsYuepeng Zhang0Guangzhong Cao1Jun Wu2Bo Gao3Linzhong Xia4Chen Lu5Hui Wang6School of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen 518060, ChinaSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaInovance Industrial Robot Reliability Technology Research Institute, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaInovance Industrial Robot Reliability Technology Research Institute, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaTo overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions.https://www.mdpi.com/2313-7673/10/5/311lower limb exoskeleton robotobstacle-crossing motiongait trajectory planning |
| spellingShingle | Yuepeng Zhang Guangzhong Cao Jun Wu Bo Gao Linzhong Xia Chen Lu Hui Wang Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots Biomimetics lower limb exoskeleton robot obstacle-crossing motion gait trajectory planning |
| title | Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots |
| title_full | Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots |
| title_fullStr | Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots |
| title_full_unstemmed | Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots |
| title_short | Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots |
| title_sort | obstacle feature information based motion decision making method for obstacle crossing motions in lower limb exoskeleton robots |
| topic | lower limb exoskeleton robot obstacle-crossing motion gait trajectory planning |
| url | https://www.mdpi.com/2313-7673/10/5/311 |
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