Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons
In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locati...
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Published in | IEEE transactions on automation science and engineering Vol. 18; no. 2; pp. 405 - 413 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Online Access | Get full text |
ISSN | 1545-5955 1558-3783 |
DOI | 10.1109/TASE.2020.3037973 |
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Abstract | In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners -For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs. |
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AbstractList | In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the exoskeleton should have the ability to help the patients to walk over different terrains. For sloped terrains, how to plan the stepping locations on slopes with different gradients and generate stable human-like gaits for patients is a critical issue. In this article, we proposed a slope gradient estimator (SGE) based on the sensor data fusion of the exoskeleton and combined SGE with the capture point theory and dynamic movement primitives (DMP) to construct an adaptive gait planning approach for slopes. After learning from demonstrated gaits sampled from healthy subjects, adaptive gait trajectories can be reproduced online to adapt to slopes with different gradients. The efficiency of the proposed approach was demonstrated on an exoskeleton system named AIDER. Experimental results indicate that the proposed approach can endow exoskeletons with the ability to generate appropriate gaits for different slopes. Note to Practitioners -For lower limb exoskeletons, it is a vital problem to plan the gait for sloped terrains. Considering different gradients among slopes, fixed predefined gait planning cannot cover all cases; thus, a slope gradient adaptive gait planning approach is necessary. The slope gradient estimator proposed in this article provides a possible slope gradient estimation method for exoskeletons or humanoid bipedal robots; it is easy to estimate the slope gradient only based on the local sensor data of the robot. The proposed dynamic gait generator provides lower limb exoskeletons and humanoid bipedal robots a possible adaptive gait planning framework and some flexibility for different slopes. The proposed approach may inspire more extended gait planning strategies for other terrains, such as stairs. |
Author | Huang, Rui Chen, Qiming Cheng, Hong Zou, Chaobin Qiu, Jing |
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Cites_doi | 10.7210/jrsj.32.503 10.1109/ICRA.2019.8793863 10.1007/978-3-642-54536-8 10.1109/ICRA.2017.7989040 10.1097/PHM.0b013e318269d9a3 10.1117/12.852726 10.1177/0278364912452673 10.3182/20120905-3-HR-2030.00165 10.1109/HUMANOIDS.2012.6651518 10.9746/sicetr1965.27.177 10.3390/electronics8030320 10.1242/jeb.017277 10.1310/sci17-00014 10.1109/IROS.2014.6943128 10.1109/TNSRE.2014.2346193 10.1109/ROBOT.2003.1241826 10.1109/HUMANOIDS.2012.6651601 10.1109/ICSMC.2003.1244649 10.1109/TNSRE.2015.2428196 10.1007/s00421-014-2955-1 10.1109/TNSRE.2012.2225640 10.1163/156855303321165097 |
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References | ref13 ref15 krause (ref12) 2012; 45 ref14 ref11 ref10 ref1 ref17 ref16 ref18 ijspeert (ref19) 2002; 15 kajita (ref22) 2014 pratt (ref24) 2006 miller (ref2) 2016; 9 ref23 ref25 ref20 ref21 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
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Snippet | In recent years, lower limb exoskeletons have gained considerable interest in applications of walking assistance for paraplegic patients. In daily lives, the... |
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SubjectTerms | Adaptive sampling Data integration Dynamic movement primitives (DMP) exoskeleton Exoskeletons Foot Gait gait planning Humanoid Legged locomotion Multisensor fusion Paraplegics Planning Robot sensing systems Robots slope Slope gradients Trajectory Walking |
Title | Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons |
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