Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks

Achic, Fred; Montero, Jhon; Peñaloza, Christian; Cuéllar, Francisco

doi:10.1109/arso.2016.7736290

Cited by 35 publications

(17 citation statements)

References 14 publications

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“…Chung et al [2] focused on autonomous drinking which involved locating the drink, picking it up and bringing it to the person's mouth. Using a hybrid BCI and head movement control, Achic et al [3] studied a setup with a moving wheelchair and an attached robotic arm. None of these systems used pure BCI control.…”

Section: Related Workmentioning

confidence: 99%

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception.In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based humanrobot interactions.

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Section: Related Workmentioning

confidence: 99%

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

View full text Add to dashboard Cite

show abstract

“…At the same time, the ability to control assistive robotic arms may radically change the self-supportiveness of these severely paralyzed individuals and fundamentally increase their quality of life [1–3]. Some research has focused on semi-automated control of robotic arms using brain-machine interfaces with promising results in laboratory settings [7, 8]. However, partly due to their often invasive character and the challenging nature of the neural recordings, those efforts have not yet resulted in systems that can be used at home by individuals with tetraplegia.…”

Section: Introductionmentioning

confidence: 99%

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Struijk

Egsgaard

Lontis

et al. 2017

J NeuroEngineering Rehabil

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BackgroundFor an individual with tetraplegia assistive robotic arms provide a potentially invaluable opportunity for rehabilitation. However, there is a lack of available control methods to allow these individuals to fully control the assistive arms.MethodsHere we show that it is possible for an individual with tetraplegia to use the tongue to fully control all 14 movements of an assistive robotic arm in a three dimensional space using a wireless intraoral control system, thus allowing for numerous activities of daily living. We developed a tongue-based robotic control method incorporating a multi-sensor inductive tongue interface. One abled-bodied individual and one individual with tetraplegia performed a proof of concept study by controlling the robot with their tongue using direct actuator control and endpoint control, respectively.ResultsAfter 30 min of training, the able-bodied experimental participant tongue controlled the assistive robot to pick up a roll of tape in 80% of the attempts. Further, the individual with tetraplegia succeeded in fully tongue controlling the assistive robot to reach for and touch a roll of tape in 100% of the attempts and to pick up the roll in 50% of the attempts. Furthermore, she controlled the robot to grasp a bottle of water and pour its contents into a cup; her first functional action in 19 years.ConclusionTo our knowledge, this is the first time that an individual with tetraplegia has been able to fully control an assistive robotic arm using a wireless intraoral tongue interface. The tongue interface used to control the robot is currently available for control of computers and of powered wheelchairs, and the robot employed in this study is also commercially available. Therefore, the presented results may translate into available solutions within reasonable time.

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“…Using a hybrid BCI and head movement control, Achic et al . [3] studies a setup with a moving wheelchair and an attached robotic arm. None of these systems use pure BCI control.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

Kuhner

Fiederer

Aldinger

et al. 2018

Preprint

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As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

show abstract

Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks

Cited by 35 publications

References 14 publications

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

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