Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante

Duburcq, Alexis; Chevaleyre, Yann; Bredèche, Nicolas; Boéris, Guilhem

doi:10.1109/icra40945.2020.9196633

Cited by 12 publications

(13 citation statements)

References 31 publications

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“…Recent years have witnessed increasing application of optimization techniques in control of LLEs in order to better meet different control objectives, which can be 1) quick adaptation to different user' condition or preference [170]- [172], 2) minimizing human joint torque or metabolic cost [173], [174], and 3) faster comfortable walking [175], etc. These techniques have been employed to perform parameter optimization [171], [173], [176], trajectory optimization [12], [172], [174], [175], [177], and control law optimization (optimal control) [178]- [180].…”

Section: ) Optimization Techniquesmentioning

confidence: 99%

“…This technique has been proven effective and robust, and has also been applied to other sophisticated robots [182]. In [177], a Guided Trajectory Learning method was first proposed to suit the need for low time complexity of online trajectory planning. Its idea is using deconvolution neural network trained by the database of trajectory optimization to predict the online trajectory.…”

Section: ) Optimization Techniquesmentioning

confidence: 99%

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Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

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Section: ) Optimization Techniquesmentioning

confidence: 99%

Section: ) Optimization Techniquesmentioning

confidence: 99%

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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“…Atalante (Fig. 1) [8], [26], [27], developed by Wandercraft, has 12 actuated joints: three at each hip, one at each knee, and two in each ankle. [9] describes the device's mechanical components and control architecture in detail.…”

Section: B Human Subject Experimentsmentioning

confidence: 99%

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Tucker

Cheng

Novoseller

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Understanding users' gait preferences of a lowerbody exoskeleton requires optimizing over the high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-inthe-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamic stability, while also highlighting inconsistencies in the utility functions underlying different users' gait preferences. This has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation.

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“…In addition to these limits, two main challenges were identified in trajectory planning for exoskeleton robots [7]. The first is trajectory optimization [7,10,64], also known as the optimal-control problem [64,65]; it is challenging to optimize a trajectory in order to suit a user's motion profile [66]. This difficulty results from user motion profiles being unique to the kinematics and dynamics of the wearer and to environmental settings.…”

Section: Challenges In Trajectory Planning Of Exoskeleton Robotsmentioning

confidence: 99%

“…Accordingly, proper trajectory planning is essential to the use of exoskeleton robots in home settings. Much work was done on trajectory planning for exoskeleton robots [8][9][10][11][12][13][14][15]. In this context, this paper deals with trajectory-planning problems, i.e., the computation of desired motion profiles for the actuation system of automatic machines.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

2020

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(1) Background: Motion planning is an important part of exoskeleton control that improves the wearer’s safety and comfort. However, its usage introduces the problem of trajectory planning. The objective of trajectory planning is to generate the reference input for the motion-control system. This review explores the methods of trajectory planning for exoskeleton control. In order to reduce the number of surveyed papers, this review focuses on the upper limbs, which require refined three-dimensional motion planning. (2) Methods: A systematic search covering the last 20 years was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct. The search strategy was to use and combine terms “trajectory planning”, “upper limb”, and ”exoskeleton” as high-level keywords. “Trajectory planning” and “motion planning” were also combined with the following keywords: “rehabilitation”, “humanlike motion“, “upper extremity“, “inverse kinematic“, and “learning machine “. (3) Results: A total of 67 relevant papers were discovered. Results were then classified into two main categories of methods to plan trajectory: (i) Approaches based on Cartesian motion planning, and inverse kinematics using polynomial-interpolation or optimization-based methods such as minimum-jerk, minimum-torque-change, and inertia-like models; and (ii) approaches based on “learning by demonstration” using machine-learning techniques such as supervised learning based on neural networks, and learning methods based on hidden Markov models, Gaussian mixture models, and dynamic motion primitives. (4) Conclusions: Various methods have been proposed to plan the trajectories for upper-limb exoskeleton robots, but most of them plan the trajectory offline. The review approach is general and could be extended to lower limbs. Trajectory planning has the advantage of extending the applicability of therapy robots to home usage (assistive exoskeletons); it also makes it possible to mitigate the shortages of medical caregivers and therapists, and therapy costs. In this paper, we also discuss challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. Commonly, methods based on the computation of swivel angles and other methods rely on the relationship (e.g., coordinated or synergistic) between the degrees of freedom used to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing configuration of the joint axis and the alignment-free configuration of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, were employed to improve the kinematic incompatibility between human and exoskeleton. Future work will focus on online trajectory planning and optimal control. This will be done because very few online methods were found in the scope of this study.

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Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante

Cited by 12 publications

References 31 publications

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

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