Task-Based Trajectory Planning for an Exoskeleton Upper Limb Rehabilitation Robot

Meng, Qiaoling; Shao, Haicun; Wang, Lulu; Yu, Hongliu

doi:10.1007/978-981-13-2481-9_18

Cited by 3 publications

(2 citation statements)

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“…These motion models can be derived from the results of the trajectory transformation corresponding to each joint movement during an ADL task. Accordingly, Meng et al proposed circular-arc trajectory planning for an upper-limb-exoskeleton rehabilitation robot [13]. For exoskeletons, once Cartesian trajectories and joint positions (through the inverse kinematics) are obtained, trajectory planning is completed in joint space using a polynomial-interpolation method.…”

Section: Approaches Based On Cartesian Motion Planningmentioning

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%

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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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Section: Approaches Based On Cartesian Motion Planningmentioning

confidence: 99%