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.
Human–Robot Interaction
In article number http://doi.wiley.com/10.1002/aisy.202200050, Geng Yang and co‐workers implement the co‐design of deep learning algorithms and the tactile sensor. By utilizing deep neural networks (DNNs), the tactile sensor can effectively detect the location and magnitude of external force and recognize different touch modalities. Besides, a novel data augmentation method is developed based on the sensor’s rotation symmetry structure, which enhances the DNNs’ generalization performance.
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
The job of the interview guide is to do a good job of checking the examinees before the interview, which has a long working procedure, long working hours and easy to be disturbed by the environment. Now design an intelligent interview guide robot to replace the guide's work. At the same time, MATLAB modeling and A* algorithm trajectory planning are used to make the interview guide robot more efficient and more intelligent.
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