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 inIEEE transactions on automation science and engineering Vol. 18; no. 2; pp. 405 - 413
Main Authors Zou, Chaobin, Huang, Rui, Qiu, Jing, Chen, Qiming, Cheng, Hong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1545-5955
1558-3783
DOI10.1109/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.
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
References_xml – ident: ref10
  doi: 10.7210/jrsj.32.503
– ident: ref5
  doi: 10.1109/ICRA.2019.8793863
– volume: 15
  start-page: 1523
  year: 2002
  ident: ref19
  article-title: Learning attractor landscapes for learning motor primitives
  publication-title: Proc Adv Neural Inf Process Syst
– year: 2014
  ident: ref22
  publication-title: Introduction to Humanoid Robotics
  doi: 10.1007/978-3-642-54536-8
– ident: ref20
  doi: 10.1109/ICRA.2017.7989040
– ident: ref1
  doi: 10.1097/PHM.0b013e318269d9a3
– ident: ref25
  doi: 10.1117/12.852726
– ident: ref23
  doi: 10.1177/0278364912452673
– volume: 45
  start-page: 165
  year: 2012
  ident: ref12
  article-title: Stabilization of the capture point dynamics for bipedal walking based on model predictive control
  publication-title: IFAC Proc Volumes
  doi: 10.3182/20120905-3-HR-2030.00165
– ident: ref11
  doi: 10.1109/HUMANOIDS.2012.6651518
– ident: ref7
  doi: 10.9746/sicetr1965.27.177
– ident: ref21
  doi: 10.3390/electronics8030320
– volume: 9
  start-page: 455
  year: 2016
  ident: ref2
  article-title: Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: Systematic review with meta-analysis
  publication-title: Research in Experimental Medicine
– ident: ref17
  doi: 10.1242/jeb.017277
– ident: ref3
  doi: 10.1310/sci17-00014
– ident: ref13
  doi: 10.1109/IROS.2014.6943128
– ident: ref14
  doi: 10.1109/TNSRE.2014.2346193
– ident: ref8
  doi: 10.1109/ROBOT.2003.1241826
– ident: ref9
  doi: 10.1109/HUMANOIDS.2012.6651601
– ident: ref4
  doi: 10.1109/ICSMC.2003.1244649
– start-page: 200
  year: 2006
  ident: ref24
  article-title: Capture point: A step toward humanoid push recovery
  publication-title: Proc 6th IEEE-RAS Int Conf Humanoid Robots
– ident: ref16
  doi: 10.1109/TNSRE.2015.2428196
– ident: ref18
  doi: 10.1007/s00421-014-2955-1
– ident: ref15
  doi: 10.1109/TNSRE.2012.2225640
– ident: ref6
  doi: 10.1163/156855303321165097
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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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