Human Cooperative Wheelchair With Brain–Machine Interaction Based on Shared Control Strategy

Li, Zhijun; Zhao, Shuai; Duan, Jiding; Su, Chun-Yi; Yang, Chenguang; Zhao, Xingang

doi:10.1109/tmech.2016.2606642

Cited by 91 publications

(41 citation statements)

References 29 publications

(30 reference statements)

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“…), while fully relying on the autonomy to generate the precise and safe low-level reactive robot motions (e.g., target approaching, collision avoidance, etc.). Researchers have exploited EEG signals for indoor navigation for a telepresence robot (Leeb et al, 2013) and the wheelchair Li Z. et al, 2017), combining left-right navigation signals with reactive robot behaviors to avoid obstacles. A system has been developed to enable users to specify a 2D end-effector path via a click-and-drag operation, and the collision avoidance is implemented with a sampling-based motion planner (Nicholas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Zeng

Shen

et al. 2020

Front. Neurorobot.

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Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

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Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Zeng

Shen

et al. 2020

Front. Neurorobot.

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“…Earlier systems such as the Bremen Autonomous Wheelchair (Lankenau et al 1998) or NavChair (Simpson et al 1998) provided safety mechanisms such as nullifying potentially hazardous input signals or incorporating obstacle avoidance behaviours adapted from algorithms developed for autonomous robots, e.g. the Vector Field Histogram (Borenstein and Koren 1991) used recently in Ashley et al (2017) and Li et al (2017). A more recent approach in reactively assistive PMDs is the weighted fusion of robot command signals with that from the user, as investigated by Devigne et al (2016), Goil et al (2013) and Urdiales et al (2011) among others.…”

Section: Introductionmentioning

confidence: 99%

Learning from demonstration for locally assistive mobility aids

Poon

Cui

Miró

et al. 2019

Int J Intell Robot Appl

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Active assistive systems for mobility aids are largely restricted to environments mapped a-priori, while passive assistance primarily provides collision mitigation and other hand-crafted behaviors in the platform's immediate space. This paper presents a framework providing active short-term assistance, combining the freedom of location independence with the intelligence of active assistance. Demonstration data consisting of on-board sensor data and driving inputs is gathered from an able-bodied expert maneuvring the mobility aid around a generic interior setting, and used in constructing a probabilistic intention model built with Radial Basis Function Networks. This allows for short-term intention prediction relying only upon immediately available user input and on-board sensor data, to be coupled with real-time path generation based upon the same expert demonstration data via Dynamic Policy Programming, a stochastic optimal control method. Together these two elements provide a combined assistive mobility system, capable of operating in restrictive environments without the need for additional obstacle avoidance protocols. Experimental results in both simulation and on the University of Technology Sydney semi-autonomous wheelchair in settings not seen in training data show promise in assisting users of power mobility aids.

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“…In particular, perceptual impairments can impact the ability to independently operate a wheelchair safely [4]. In this context, several research teams developed autonomous or semi-autonomous wheelchair navigation solutions [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Design of a Haptic Guidance Solution for Assisted Power Wheelchair Navigation

Devigne¹,

Pasteau²,

Babel³

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Smart powered wheelchairs can increase mobility and independence for people with disability by providing navigation support. This support can be supplied in the form of autonomous or semi-autonomous obstacle avoidance systems. However, for rehabilitation or learning purposes, it would be of great benefit for wheelchair users to have a better understanding of the surrounding environment while driving. Therefore, another way of providing navigation support is to communicate information through a dedicated and adapted feedback interface. We here propose a framework in which feedback is provided by sending forces through the wheelchair controller as the user steers the wheelchair. This solution is based on a low complex optimization framework able to perform smooth trajectory correction and to provide obstacle avoidance. The impact of the proposed haptic guidance solution on user driving performance was assessed during this pilot study for validation purposes through an experiment with 4 able-bodied participants. They were asked to drive a power wheelchair on an obstacle course with and without activation of the force feedback. Results of this pilot study showed that the number of collisions significantly decreased while force feedback was activated, thus validating the proposed framework.

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Human Cooperative Wheelchair With Brain–Machine Interaction Based on Shared Control Strategy

Cited by 91 publications

References 29 publications

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Learning from demonstration for locally assistive mobility aids

Design of a Haptic Guidance Solution for Assisted Power Wheelchair Navigation

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