The Classification and New Trends of Shared Control Strategies in Telerobotic Systems: A Survey

Li, Gaofeng; Li, Qiang; Yang, Chenguang; Su, Yuan; Zhang, Yuan; Wang, Xinyu

doi:10.1109/toh.2023.3253856

Cited by 9 publications

(3 citation statements)

References 121 publications

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“…The concept of shared control has been applied in many research fields, such as teleoperated robots [ 95 , 96 ], brain–machine interfaces [ 97 , 98 ], autonomous driving [ 99 ], and surgical assistive robots [ 100 ]. According to the latest review on shared control schemes applied to teleoperated robots [ 101 ], existing shared control schemes can be classified into semi-autonomous control (SAC), state-guided shared control (SGSC), and state-fusion shared control (SFSC) based on the different ways of sharing between human users and autonomous controllers. Although SGSC and SFSC, which offer richer interaction means and more intelligent operations, are the future development trend, SAC, which is relatively simple but more practical, more user-friendly, and better aligned with the design intention of shared control schemes to combine the advantages of human users and intelligent controllers, remains more popular and preferred for enhancing task performance and reducing user burden.…”

Section: Current Research Statusmentioning

confidence: 99%

A Review of Myoelectric Control for Prosthetic Hand Manipulation

et al. 2023

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Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

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Section: Current Research Statusmentioning

confidence: 99%

A Review of Myoelectric Control for Prosthetic Hand Manipulation

et al. 2023

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“…A lot of research has focused on helping people with disabilities move around, and smart wheelchairs have been developed using various technologies and tools. When we look at the studies, they categorize navigation methods into three groups based on how much assistance they provide [18], [20], [21], [101]. These groups are shared control, fully autonomous, and semi-autonomous control.…”

Section: ) Wheelchair Robotmentioning

confidence: 99%

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Jayasekara,

Kulathunge,

Premaratne

et al. 2023

Journal of Robotics and Control

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This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

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“…Therefore, shared autonomy approaches have been proposed in the literature to leverage the capabilities of autonomous systems to complement the skills of human operators [20]. Human intelligence is superior in making intelligent and flexible high-level decisions, dealing with unexpected events, and comprehending the varying and unconstructed environment., while machine autonomy has the advantage of high stability, accuracy and fast responses [21,22].…”

Section: Introductionmentioning

confidence: 99%

Visual-Aided Shared Control of Semi-Autonomous Underwater Vehicle for Efficient Underwater Grasping

Wang,

Ding,

Sun

2023

JMSE

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Human intelligence has the advantage for making high-level decisions in the remote control of underwater vehicles, while autonomous control is superior for accurate and fast close-range pose adjustment. Combining the advantages of both remote and autonomous control, this paper proposes a visual-aided shared-control method for a semi-autonomous underwater vehicle (sAUV) to conduct flexible, efficient and stable underwater grasping. The proposed method utilizes an arbitration mechanism to assign the authority weights of the human command and the automatic controller according to the attraction field (AF) generated by the target objects. The AF intensity is adjusted by understanding the human intention, and the remote-operation command is fused with a visual servo controller. The shared controller is designed based on the kinematic and dynamic models, and model parameter uncertainties are also addressed. Efficient and stable control performance is validated by both simulation and experiment. Faster and accurate dynamic positioning in front of the target object is achieved using the shared-control method. Compared to the pure remote operation mode, the shared-control mode significantly reduces the average time consumption on grasping tasks for both skilled and unskilled operators.

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The Classification and New Trends of Shared Control Strategies in Telerobotic Systems: A Survey

Cited by 9 publications

References 121 publications

A Review of Myoelectric Control for Prosthetic Hand Manipulation

A Review of Myoelectric Control for Prosthetic Hand Manipulation

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Visual-Aided Shared Control of Semi-Autonomous Underwater Vehicle for Efficient Underwater Grasping

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