Motion planning of upper-limb exoskeleton robots : a review

ABSTRACT: 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. 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 “. 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. 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

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented

A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

Robotics to support elderly people in living independently and to assist disabled people in carrying out the activities of daily living independently have demonstrated good results. Basically, there are two approaches: one of them is based on mobile robot assistants, such as Care-O-bot, PR2, and Tiago, among others; the other one is the use of an external robotic arm or a robotic exoskeleton fixed or mounted on a wheelchair. In this paper, a modular mobile robotic platform to assist moderately and severely impaired people based on an upper limb robotic exoskeleton mounted on a robotized wheel chair is presented. This mobile robotic platform can be customized for each user’s needs by exploiting its modularity. Finally, experimental results in a simulated home environment with a living room and a kitchen area, in order to simulate the interaction of the user with different elements of a home, are presented. In this experiment, a subject suffering from multiple sclerosis performed different activities of daily living (ADLs) using the platform in front of a group of clinicians composed of nurses, doctors, and occupational therapists. After that, the subject and the clinicians replied to a usability questionnaire. The results were quite good, but two key factors arose that need to be improved: the complexity and the cumbersome aspect of the platform.This work was supported by the AIDE project through Grant Agreement No. 645322 of the European Commission, by the Conselleria d’Educacio, Cultura i Esport of Generalitat Valenciana, by the European Social Fund—Investing in your future, through the grant ACIF 2018/214, and by the Promoción de empleo joven e implantación de garantía juvenil en I+D+I 2018 through the grant PEJ2018-002670-A

User Based Development and Test of the EXOTIC Exoskeleton:Empowering Individuals with Tetraplegia Using a Compact, Versatile, 5-DoF Upper Limb Exoskeleton Controlled through Intelligent Semi-Automated Shared Tongue Control

This paper presents the EXOTIC- a novel assistive upper limb exoskeleton for individuals with complete functional tetraplegia that provides an unprecedented level of versatility and control. The current literature on exoskeletons mainly focuses on the basic technical aspects of exoskeleton design and control while the context in which these exoskeletons should function is less or not prioritized even though it poses important technical requirements. We considered all sources of design requirements, from the basic technical functions to the real-world practical application. The EXOTIC features: (1) a compact, safe, wheelchair-mountable, easy to don and doff exoskeleton capable of facilitating multiple highly desired activities of daily living for individuals with tetraplegia; (2) a semi-automated computer vision guidance system that can be enabled by the user when relevant; (3) a tongue control interface allowing for full, volitional, and continuous control over all possible motions of the exoskeleton. The EXOTIC was tested on ten able-bodied individuals and three users with tetraplegia caused by spinal cord injury. During the tests the EXOTIC succeeded in fully assisting tasks such as drinking and picking up snacks, even for users with complete functional tetraplegia and the need for a ventilator. The users confirmed the usability of the EXOTIC

Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild

While humans can use parts of their arms other than the hands for manipulations like gathering and supporting, whether robots can effectively learn and perform the same type of operations remains relatively unexplored. As these manipulations require joint-level control to regulate the complete poses of the robots, we develop AirExo, a low-cost, adaptable, and portable dual-arm exoskeleton, for teleoperation and demonstration collection. As collecting teleoperated data is expensive and time-consuming, we further leverage AirExo to collect cheap in-the-wild demonstrations at scale. Under our in-the-wild learning framework, we show that with only 3 minutes of the teleoperated demonstrations, augmented by diverse and extensive in-the-wild data collected by AirExo, robots can learn a policy that is comparable to or even better than one learned from teleoperated demonstrations lasting over 20 minutes. Experiments demonstrate that our approach enables the model to learn a more general and robust policy across the various stages of the task, enhancing the success rates in task completion even with the presence of disturbances. Project website: https://airexo.github.io/Comment: Project page: https://airexo.github.io