| 1. |
- Kong, Depeng, et al.
(författare)
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Bioinspired Co-Design of Tactile Sensor and Deep Learning Algorithm for Human-Robot Interaction
- 2022
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : Wiley. - 2640-4567. ; 4:6
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Tidskriftsartikel (refereegranskat)abstract
- Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co-design of a tactile sensor and a deep learning-based algorithm is proposed herein, simplifying the sensor structure while providing computation-enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black-coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo-data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well-generalized performance, and providing a promising application prospect in human-robot interaction.
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| 2. |
- Lyu, Honghao, et al.
(författare)
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GuLiM : A Hybrid Motion Mapping Technique for Teleoperation of Medical Assistive Robot in Combating the COVID-19 Pandemic
- 2022
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2576-3202. ; 4:1, s. 106-117
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Tidskriftsartikel (refereegranskat)abstract
- Driven by the demand to largely mitigate nosocomial infection problems in combating the coronavirus disease 2019 (COVID-19) pandemic, the trend of developing technologies for teleoperation of medical assistive robots is emerging. However, traditional teleoperation of robots requires professional training and sophisticated manipulation, imposing a burden on healthcare workers, taking a long time to deploy, and conflicting the urgent demand for a timely and effective response to the pandemic. This paper presents a novel motion synchronization method enabled by the hybrid mapping technique of hand gesture and upper-limb motion (GuLiM). It tackles a limitation that the existing motion mapping scheme has to be customized according to the kinematic configuration of operators. The operator awakes the robot from any initial pose state without extra calibration procedure, thereby reducing operational complexity and relieving unnecessary pre-training, making it user-friendly for healthcare workers to master teleoperation skills. Experimenting with robotic grasping tasks verifies the outperformance of the proposed GuLiM method compared with the traditional direct mapping method. Moreover, a field investigation of GuLiM illustrates its potential for the teleoperation of medical assistive robots in the isolation ward as the Second Body of healthcare workers for telehealthcare, avoiding exposure of healthcare workers to the COVID-19.
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