Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Yang, Xiaolong; Chen, Fu‐Rong; Wang, Feilong; Zheng, Long; Wang, Shukun; Qi, Wen; Su, Hang

doi:10.3390/biomimetics8020169

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In 2020, Yangyang Zhang et al used wearable data belts and data gloves to accurately measure the joint motion of shoulder joints, elbow joints, wrist joints, metacarpal bones, and proximal finger joints and experimentally verified the effectiveness of the proposed method [42]. More, we have conducted related work to integrate Kinect with data gloves [43].…”

Section: Introductionmentioning

confidence: 92%

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm

Chen,

Wang,

Dong

et al. 2023

Bioengineering

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The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

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Section: Introductionmentioning

confidence: 92%

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm

Chen,

Wang,

Dong

et al. 2023

Bioengineering

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“…Our method ensures precise control of the bionic hand, even in scenarios where sensors may be obstructed or fall out of their field of view. In our previous work, we developed an anthropomorphic dual-arm robot with a bionic hand, which contains two humanoid arms with 5 DOF each, including 2-shoulder DOF, 2-elbow DOF, and 1-wrist DOF [46]. The two arms are mainly responsible for the movement of the end-effector within a certain space, which is a bionic hand with six degrees of freedom, including two degrees for the thumb and one degree for each of the other four fingers.…”

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confidence: 99%

Whole-Body Teleoperation Control of Dual-Arm Robot Using Sensor Fusion

Wang,

Chen,

Dong

et al. 2023

Biomimetics

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As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.

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