Soft Wearable Robots: Development Status and Technical Challenges

Shi, Yongjun; Dong, Wei; Lin, Weiqi; Gao, Yongzhuo

doi:10.3390/s22197584

Cited by 27 publications

(18 citation statements)

References 133 publications

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“…The human body serves as a great source of energy generation, and considerable amounts of kinetic, potential, and thermal energy are generated during normal limb activity [1][2][3][4]. The use of these energies has been neglected in past studies until the miniaturization of body sensors and the emergence of many wearable devices [5][6][7][8]. Numerous researchers have begun to explore how these energies can be used to power them for longer periods [9][10][11][12].…”

Section: Literature Reviewmentioning

confidence: 99%

Active self-powered human motion assist system

Ren,

Zhou,

Zhang

et al. 2024

Smart Mater. Struct.

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Harvesting human energy currently occurs to power wearable devices or monitor human signs, these applications make the energy harvester less applicable to and seldom used for assisting human motion. However, there is a high demand for using such collected energy with the assistance of human motion. This paper presents a novel energy harvester that is designed to collect negative work, assist human motion, and realize self-powering. An active self-powered human motion assist system (HMAS) is developed. The system consists of a human motion assist device (HMAD), a flexible rack, an electronic circuit module, and a supercapacitor. The HMAS can collect negative work from the human body, provide the user with additional motion assistance, and reduce stamina consumption. A series of experiments verify that HMAS has a high negative work collection power and a high energy conversion efficiency. The average output power is 0.93 W measured by the negative work collection test bed at a simulated knee bend angle of 40° and a frequency of 2 Hz. The energy conversion efficiency is up to 48.2%. Human motion assistance experiments verify that HMAS can provide volunteers with up to 2.57% assisting moment and minimize the metabolic cost of volunteers by 6.07% compared to without wearing HMAS. This research work is proposed to contribute to the development of active self-powered exoskeleton technology. This technology can be practically applied in the fields of rehabilitation therapy, logistics transportation, and military combat.

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Section: Literature Reviewmentioning

confidence: 99%

Active self-powered human motion assist system

Ren,

Zhou,

Zhang

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…Owing to the difference in the method of implementation, obstacle detection can be divided into sensor-based and map-based methods. Among the sensor-based methods, the vision-based method (Shi et al , 2022; Mancini et al , 2018) was developed earlier, but this method has a limited ability to detect the specific size of obstacles. Naujoks et al (Naujoks and Wuensche, 2018) calculated the three-dimensional direction correction frame of an obstacle using point cloud data of a three-dimensional lidar, which can obtain the spatial pose and size of obstacles.…”

Section: Introductionmentioning

confidence: 99%

Towards an obstacle detection system for robot obstacle negotiation

Wang,

Zhang,

Fan

et al. 2024

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Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

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“…With the advent of the artificial intelligence era, robotic manipulators have also been endowed with "intelligence," and are widely used in more and more fields. 1,2 Regrettably, the transmission mechanism of the traditional manipulator, which typically includes gears, lead screws, or belts, renders it more susceptible to factors like friction, deflection, backlash, and compressibility. Consequently, the transmission mechanism is prone to aging and wear, resulting in a decrease in the positioning accuracy of the manipulator.…”

Section: Introductionmentioning

confidence: 99%

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

Liu,

Chen,

Xie

2024

Adaptive Control & Signal

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SummaryThe direct drive manipulator, which directly links the joint shaft to the motor rotor, is susceptible to external disturbances and model perturbations. When the direct drive manipulator operates with varying payloads, the disturbance observer (DOB) with a constant nominal model struggles to achieve satisfactory performance. To minimize estimation errors caused by disturbance, enhance trajectory tracking accuracy, and improve system robustness, this article proposes a controller parameter self‐tuning strategy with settling time, fused with a nominal model adaptive disturbance observer. Specifically, by optimizing the gain coefficient of the model reference adaptive identification (MRAI), the significant changes in payload at the manipulator's end joint can be quickly and accurately estimated. The inertia introduced by the payload is then employed to continuously update the nominal model of the manipulator, incorporating information about the inertia of the coupling joints. In addition, the controller parameters are updated using a self‐tuning link with settling time, which guarantees that the closed‐loop characteristics of the system remain unaffected by payload changes. Finally, the proposed method was validated using a two‐joint direct drive manipulator. The results demonstrate the robustness of the proposed method in the presence of significant payload changes, and the accuracy of disturbance estimation and the tracking performance of the proposed control strategy is significantly improved.

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Soft Wearable Robots: Development Status and Technical Challenges

Cited by 27 publications

References 133 publications

Active self-powered human motion assist system

Active self-powered human motion assist system

Towards an obstacle detection system for robot obstacle negotiation

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

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