Advanced Intelligent Systems

Magnetically manipulated interventional robotic systems offer outstanding advantages for improving vascular interventions, including minimizing radiation exposure to physicians and increasing the controllability of magnetic interventional devices in hard‐to‐reach vessels. However, automatic control of magnetic guidewires (MGs) is still challenging in terms of modeling of guidewires and trajectory planning. Herein, a magnetically controlled guidewire robotic system (MCGRS) with steering and propulsion capabilities is proposed based on adequate modeling and trajectory planning methods. The steering kinematics of MG is first modeled by constant curvature theory. Then, a continuum mechanics model is built to predict the deformation of the magnetic tip by combining the dipole model and the Cosserat‐rod model. Moreover, a trajectory planning algorithm is developed to navigate the MG through vessels. Furthermore, trajectory following experiments within three vascular phantoms confirm that the proposed model and algorithm are reliable and capable of navigating the MG through the desired trajectory. Finally, two extra navigation experiments are implemented in 3D vascular phantom, which show that the MCGRS can be remotely controlled to manipulate the MG to actively steer and reach the target site. The system and methods will build the foundation for automatic control of MG and help to improve the autonomy.

Shixiong Fu

Binghan Chen

Dong Li

Jianguo Han

Sheng Xu

Shu Wang

Chenyang Huang

Ming Qiu

Si Cheng

Xinyu Wu

Li Zhang

Shiwei Du

Tiantian Xu

A Magnetically Controlled Guidewire Robot System with Steering and Propulsion Capabilities for Vascular Interventional Surgery

用于血管介入手术的具有转向和推进能力的磁控导丝机器人系统

The Braitenberg vehicle as a simple conceptual model to characterize the response behaviors of animals or insects under a stimulus is widely used to develop autonomous vehicles able to adapt to the varying environments. Considerable effort has been devoted to building neuromorphic processors with the software in the vehicles; however, there has been no demonstration of Braitenberg vehicle with neuromorphic hardware so far. Herein, a Braitenberg vehicle with simple memristive neuromorphic circuits is built for the first time. This vehicle exhibits adaptive behaviors in the supervised learning process and is eventually trained to conduct the task of tracking path. Moreover, the memristive circuit in the vehicle demonstrates a very short response latency (≈56 ns) to input sensory information. Herein, an alternative promising solution to build self‐adaptive robots and pave the way for the realization of autonomous robots based on memristive neuromorphic circuits is offered.

Cong Wang

Zaizheng Yang

Shuang Wang

Pengfei Wang

Chen-Yu Wang

Chen Pan

Bin Cheng

Shi-Jun Liang

Feng Miao

A Braitenberg Vehicle Based on Memristive Neuromorphic Circuits

基于忆阻神经形态电路的Braitenberg车辆

Sensory adaptation plays a critical role in humans interacting with the environment. Inspired by humans, realization of sensory adaptation on robots can make them adapt to the environment gradually. The gradual change of sensitivity that depends on recent experience of external stimuli is the most important process for the adaptation. To realize sensory adaptation, such change of sensitivity needs to be realized. It is proposed to fabricate the memristor based on single‐crystalline LiNbO 3 thin film. The resistance of the memristor can be changed monotonically and gradually with the increase in the number of voltage pulses, which can be ascribed to the property of single‐crystalline thin films. Based on the characteristic, it is proposed to use the memristor as artificial synapse of the proposed bioinspired system, using conductance of the memristor to denote susceptibility value to realize the gradual change of sensitivity by recent external stimuli. A novel general excitation method of signals from multimodal sensors on memristor is proposed and utilized in the signal‐coupling module of the system, which makes the system realize sensory adaptation for different stimuli accepted by multimodal sensors. Using artificial sensory memory systems, sensory adaptation on robot is realized for the first time herein.

Xinqiang Pan

Jiejun Wang

Zhen Deng

Yao Shuai

Wenbo Luo

Wen Luo

Qin Xie

Yao Xiao

Song Tang

Shuwen Jiang

Chuangui Wu

Feng Zhu

Jianwei Zhang

Wanli Zhang

A Memristor‐Based Bioinspired Multimodal Sensory Memory System for Sensory Adaptation of Robots

基于忆阻器的仿生多模态感觉记忆系统用于机器人感觉适应

Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

Tian Wang

Pai Zheng

Shufei Li

Lihui Wang

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

以人为中心的智能制造中的多模态人机交互：综述

Over the past decade, soft robot research has expanded to diverse fields, including biomedicine, bionics, service robots, human–robot interaction, and artificial intelligence. Much work has been done in modeling the kinematics and dynamics of soft robots, but closed‐loop control is still in its early stages due to limited sensory feedback. Thanks to the advancement in functional materials, structures, and manufacturing techniques for flexible electronics, flexible and stretchable sensors are developing rapidly. These sensors provide feedback for closed‐loop control tasks and enable soft robots to effectively explore the unknown and safely interact with humans and the environment. Herein, recent advances in perceptive soft robots that utilize flexible/stretchable sensors and functional materials are outlined. The perceptive functions of soft robots from two different aspects, that is, proprioception and exteroception, are summarized. Furthermore, the constructions of autonomous soft robots by integrating both proprioceptive and exteroceptive capabilities for closed‐loop control tasks and other challenging tasks in the real world are discussed.

Zhuofan Lin

Ziya Wang

Wenyu Zhao

Yingtian Xu

Xiaopu Wang

Tianwei Zhang

Zhenglong Sun

Lan Lin

Zhengchun Peng

Recent Advances in Perceptive Intelligence for Soft Robotics

软体机器人感知智能的最新进展

In multisensory storytelling, the integration of touch, sound, speech, and visual elements plays a crucial role in enhancing the narrative immersion and audience engagement. In light of this, this article presents a scalable and intelligent hybrid artificial intelligence (AI) method that uses emotional text analysis for deciding when and what midair haptics to display alongside audiovisual content generated by latent stable diffusion methods. Then, a user study involving 40 participants is described, the results of which suggest that the proposed approach enhances the audience level of engagement as they experience a short AI‐generated multisensory (audio–visual–haptic) story.

Maciej Stroinski

Kamil Kwarciak

Mateusz Kowalewski

Daria Hemmerling

William Frier

Orestis Georgiou

Text‐to‐Haptics: Enhancing Multisensory Storytelling through Emotionally Congruent Midair Haptics

文本转触觉：通过情感一致的空中触觉增强多感官叙事

Despite significant progress in developing wearable systems for hand tracking, most devices are still bulky, restrictive to the user or to the placement of the exoskeleton systems, and sensitive to skin preparation and impedance. In this work, we develop a wristband that integrates an array of 10 skin‐conformal strain sensors based on laser‐induced graphene, which is optimized for continuous measurement of skin strain. The device is characterized to identify several hand gestures and tasks while simultaneously using an optical camera‐based hand‐tracking system to estimate the joint locations for ground truth generation. Machine learning models are developed to predict gestures as well as specific hand joint angles with high accuracy of &gt;90% and &gt;95%, respectively. The findings show that the sensors placed closer to actuation‐specific anatomical features contribute more toward the high accuracy. The sensor array is also integrated with a wearable readout system that wirelessly transmits the data in real time in order to control a robotic arm as a proof of concept for human–robot interaction applications. The developed skin‐conformal device is expected to find wide applications in rehabilitation, sports sciences, and human–computer interaction, paving the way for low‐profile prosthetic and orthotic control systems.

Vinay Kammarchedu

Monther AlSiyabi

Aida Ebrahimi

Skin‐Conformal Myography for Real‐Time Hand Tracking Using a Laser‐Induced Graphene Strain Sensor Array

基于激光诱导石墨烯应变传感器阵列的实时手部追踪皮肤共形肌电图

Autonomous, cloud‐based laboratories (CBLs) are transforming scientific research by democratizing access to advanced instruments that accelerate high‐throughput discovery. As artificial intelligences (AIs) become integrated or “embodied” with CBLs and gain independence from human oversight, efforts to identify novel pharmaceuticals, renewable energies, and agricultural biotechnologies will accelerate. AI‐driven CBLs can perform tasks more efficiently and accurately than human scientists at lower costs, achieving results in weeks rather than years. However, as AI systems approach or exceed human intelligence, their decision‐making abilities could outpace the need for human input, raising ethical, economic, and safety concerns. Aligning AI goals with human values is critical, as unregulated systems could pose existential risks, including global health hazards or the distortion of knowledge‐generating systems. AI‐driven misinformation in research highlights the need for transparency and data integrity, which may be achieved by aligning incentivizes and engineered fail‐safes to promote long‐term human flourishing. To mitigate risks, strict compartmentalization of AI systems and CBLs with third‐party supervision at fine temporal resolutions will be necessary. While current CBLs are piloted by humans, future AI systems may relegate humans to the role of co‐pilot. Anticipating increased AI‐CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm.

Nicolas Rouleau

Nirosha J. Murugan

The Risks and Rewards of Embodying Artificial Intelligence with Cloud‐Based Laboratories

基于云实验室的人工智能具身化的风险与回报

Inherent noise characteristics of memristor devices can be utilized in stochastic computing applications such as true random number generators (TRNGs). However, the ratio between capture and emission time can significantly affect the randomness of generated bit streams by TRNGs. Herein, a bias‐independent TRNG circuit is presented, utilizing the random telegraph noise (RTN) signal of the memristor as a random entropy source. This design considers the condition‐dependent RTN characteristics, including capture time and emission time constants, which vary with read voltage ( V read ) and temperature conditions in the high‐resistance state of the fabricated memristor. The TRNG circuit, comprising an edge detection circuit and an N ‐bit counter, is experimentally demonstrated to validate hardware feasibility with the optimized external clock frequency, which can mitigate the biases induced by V read and temperature. Finally, the performance of the designed TRNG circuit is evaluated using autocorrelation functions and National Institute of Standards and Technology tests, confirming its capability to produce random number bitstreams.

Jinwoo Park

Hyunjoong Kim

Hyungjin Kim

Bias‐Independent True Random Number Generator Circuit using Memristor Noise Signals as Entropy Source

使用忆阻器噪声信号作为熵源的无偏置真随机数生成器电路

Touchscreens are essential parts of many electronics in daily lives of sighted people in the digital information era. On the other hand, visually impaired users rely on tactile displays as one of the key communication devices to interact with the digital world. However, due to their working mechanism and the uneven surface of tactile displays, one of the key features of screens for sighted users is surprisingly challenging to implement: precision touch input. To overcome this, a hand gesture recognition system is developed using a frequency‐modulated continuous wave millimeter‐wave radar. A multifeature encoder method is used to obtain the range and velocity information from the radar to translate the data into spectrogram images. Gesture recognition is implemented for common input gestures: single/double‐click, swipe‐right/left, scroll‐up/down, zoom‐in/out, and rotate‐anticlockwise/clockwise. The gesture recognition and classification are based on machine learning, support vector machines, deep learning, and convolutional neural network approaches. The chosen model You‐Only‐Look‐Once (YOLOv8) shows a high accuracy of 97.1% by iterating only 30 epochs with only 500 collected data samples per gesture. This research paves the way toward using radar sensors not only for tactile displays but also for other digital devices in human–computer interaction.

Ahmed Hamza

Santosh Kumar Prabhulingaiah

Pegah Pezeshkpour

Bastian E. Rapp

Hand Gesture Recognition Using Frequency‐Modulated Continuous Wave Radar on Tactile Displays for the Visually Impaired

用于视觉障碍者的触觉显示器上使用调频连续波雷达的手势识别

Abbreviation	Adv. Intell. Syst.
Journal Impact	6.69
Quartiles(Global)	ROBOTICS(Q1)

Publication Information	Publisher: John Wiley and Sons Inc，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2023，Proportion of original research papers: 92.27%，Self Citation Rate:8.80%， Gold OA Rate: 86.43%
Journal Fees	$3510Details
Average review cycle	网友分享经验：4 Weeks

Advanced Intelligent Systems

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