Planar location of the simulative acoustic source based on fiber optic sensor array

A fiber optic sensor array which is structured by four Sagnac fiber optic sensors is proposed to detect and locate a simulative source of acoustic emission (AE). The sensing loops of Sagnac interferometer (SI) are regarded as point sensors as their small size. Based on the derived output light intensity expression of SI, the optimum work condition of the Sagnac fiber optic sensor is discussed through the simulation of MATLAB. Four sensors are respectively placed on a steel plate to structure the sensor array and the location algorithms are expatiated. When an impact is generated by an artificial AE source at any position of the plate, the AE signal will be detected by four sensors at different times. With the help of a single chip microcomputer (SCM) which can calculate the position of the AE source and display it on LED, we have implemented an intelligent detection and location.     

An Intelligent Robotic System Capable of Sensing and Describing Objects Based on Bimodal,Self-Powered Flexible Sensors

This study presents an intelligent soft robotic system capable of perceiving, describing, and sorting objects based on their physical properties. This work introduces a bimodal self-powered flexible sensor (BSFS) based on the triboelectric nanogenerator and giant magnetoelastic effect. The BSFS features a simplified structure comprising a magnetoelastic conductive film and a packaged liquid metal coil. The BSFS can precisely detect and distinguish touchless and tactile models, with a response time of 10 ms. By seamlessly integrating the BSFSs into the soft fingers, this study realizes an anthropomorphic soft robotic hand with remarkable multimodal perception capabilities. The touchless signals provide valuable insights into object shape and material composition, while the tactile signals offer precise information regarding surface roughness. Utilizing a convolutional neural network (CNN), this study integrates all sensing information, resulting in an intelligent soft robotic system that accurately describes objects based on their physical properties, including materials, surface roughness, and shapes, with an accuracy rate of up to 97%. This study may lay a robotic foundation for the hardware of the general artificial intelligence with capacities to interpret and interact with the physical world, which also serves as an interface between artificial intelligence and soft robots.     

Controlling mobile robots in distributed intelligent sensor network

Mobile robots need sufficient sensors and information on the environment in order to navigate. In this paper, we propose a system of mobile robots, which is controlled in a distributed intelligent sensor network. In such a networked space, the environment is divided by distributed sensors. Each area is monitored by a distributed sensor device, which connects with other distributed sensor devices and robots throughout the network. As a result, the mobile robots are able to accomplish tasks simply by following orders from the sensor devices in the networked environment, although the mobile robots are not self-contained with information on the environment and sensors for self-positioning and control. We test several situations to verify the proposed system.     

Soft Robotic-Adapted Multimodal Sensors Derived from Entirely Intrinsic Self-Healing and Stretchable Cross-Linked Networks

Flexible sensing technologies that play a pivotal role in endowing robots with detection capabilities and monitoring their motions are impulsively desired for intelligent robotics systems. However, integrating and constructing reliable and sustainable flexible sensors with multifunctionality for robots remains an everlasting challenge. Herein, an entirely intrinsic self-healing, stretchable, and attachable multimodal sensor is developed that can be conformally integrated with soft robots to identify diverse signals. The dynamic bonds cross-linked networks including the insulating polymer and conductive hydrogel with good comprehensive performances are designed to fabricate the sensor with prolonged lifespan and improved reliability. Benefiting from the self-adhesiveness of the hydrogel, strong interfacial bonding can be formed on various surfaces, which promotes the conformable integration of the sensor with robots. Due to the ionic transportation mechanism, the sensor can detect strain and temperature based on piezoresistive and thermoresistive effect, respectively. Moreover, the sensor can work in triboelectric mode to achieve self-powered sensing. Various information can be identified from the electrical signals generated by the sensor, including hand gestures, soft robot crawling motions, a message of code, the temperature of objects, and the type of materials, holding great promise in the fields of environmental detection, wearable devices, human-machine interfacing, and robotics.     

A flexible temperature sensing finger using optical fiber grating for soft robot application

Bionic perception, especially temperature-aware, is one of the important issues in soft robots research. This paper presents a method to implant temperature sensor network into soft robot finger by using optical fiber gratings. For avoiding strain disturbance, a dedicated metal tube is designed to package and protect the optical fiber gratings. For implanting the sensors firmly, a solution using two kinds of adhesive is proposed. The prototype is calibrated in high precision temperature bath and then measured under different temperatures and different bending conditions, respectively. The experimental results are compared with electronic temperature sensor (PT100 thermal resistance), which verifies the accuracy and repeatability of the design. With the dedicated coating and adhesive solution, the proposed temperature sensor network is suitable for implanting into soft robots for the temperature-aware ability.     

Behavior-based artificial intelligence in miniature mobile robot

An intelligent mobile robot that implements concepts of mechatronics, mobile robotics, and behavior-based artificial intelligence has been developed. The design process for such a mobile robot employs a concept of simple trade-off between the mechanical, electrical, and computational systems for the benefit of improving the robots overall system performance. The main features of the robot, named SCAVENGER, are to navigate freely in a controlled environment, search for one or more target objects with known characteristics (colored golf balls), and avoid the other objects as obstacles. A combination of infrared sensors, color detectors, and bumper skirts is used to facilitate navigation and object recognition. The robots brain consists of an on-board MC68HC11 microcontroller, which is programmed in C. The control software implements behavior-based artificial intelligence, where the robots overall intelligence is made up of layers of several simple and primitive behaviors similar to those observed in insects. In this work, the design of the robots subsystems, where the implementation of mechatronics is most in effect, is covered in more detail with a particular interest in object recognition and collection systems.     

Wireless Monitoring of Small Strains in Intelligent Robots via a Joule Heating Effect in Stretchable Graphene–Polymer Nanocomposites

Flexible strain sensors are an important component for future intelligent robotics. However, the majority of current strain sensors must be electrically connected to a corresponding monitoring system via conducting wires, which increases system complexity and restricts the working environment for monitoring strains. Here, stretchable graphene–polymer nanocomposites that act as strain sensors using a Joule heating effect are reported. When the resistance of the sensor changes in response to a strain, the resulting change in temperature is wirelessly detected in an intelligent robot. By engineering and optimizing the surface structure of graphene–polymer nanocomposites, the fabricated strain sensors exhibit excellent stability when subjected to periodic temperature signals over 400 cycles while being periodically strained and deliver a high strain sensitivity of 7.03 × 10^?4 °C^?1 %^?1 for strain levels of 0% to 30%. As a wearable electronic device, the approach provides the capability to wirelessly monitor small strains for intelligent robots at a high strain resolution of ≈0.1%. Moreover, when the strain sensing system operates as a multichannel structure, it allows precise strain detection simultaneously, or in sequence, for each finger of an intelligent robot.     

Progresses in Tensile,Torsional, and Multifunctional Soft Actuators

Soft actuators have received intensive attention in the fields of soft robots, sensors, intelligent control, artificial intelligence, and visual intelligence. By combination of tensile and torsional deformations, different types of motions can be realized, such as bending, rolling, and jumping. Soft robotics need soft actuators, such as artificial muscles to lift or move objects to perform some work. Additionally, actuation integrated with functions of sensing, signal transmission, and control is also needed in the development of advanced intelligent systems, which further stimulates the requirement of multifunctional actuators. Here different types of soft actuators that can perform tensile and torsional actuations are summarized, including twisted fiber artificial muscles, shape memory polymers, hydrogels, liquid crystal polymers, electrochemical actuators with conducting polymers, and some natural materials. Examples are also included regarding the bending or rolling deformations of the actuators for lifting objects. Then, recent interesting reports about multifunctional soft actuators combined with sensing and signal transmission performances, are summarized. Last, a summary of different ways to realize tensile and torsional actuations, different materials, and designs for lifting or moving objects, as well as construction of multifunctional actuators with actuation and sensing functions is provided.     

基于模糊神经网络系统的智能小车避障

智能小车把超声波传感器和红外传感器相结合来感知外界环境的信息,并按照一定的规则来调整小车的方位角和速度,实现智能小车的自主导航和避障。模糊神经网络作为人工智能的分支,兼具模糊逻辑系统和神经网络各自的优点,具有表达和处理确定的信息、模糊信息的能力和良好的学习能力等特点。把模糊逻辑系统和神经网络结合起来,运用到智能小车避障的自适应控制中,并且使用一种多层前馈型神经网络即BP神经网络在模糊神经系统中解决神经网络的权系数优化问题。     

Design and calibration of 3D printed soft deformation sensors for soft actuator control

Soft actuators made from compliant materials are superior to conventional rigid robots in terms of flexibility, adaptability and safety. However, an inherent drawback of soft actuator is the low actuation precision. Implementing closed loop control is a possible solution, but the soft actuator shape can hardly be measured directly by commercially available sensors, which either are too stiff for integration or cause performance degradation of the actuator. Although 3D printing has been applied to print bendable sensors from conductive materials, they either have larger stiffness than the soft actuator or are made from specially designed materials that are difficult to reproduce. In this study, easily accessible commercial soft conductive material is applied to directly 3D print soft sensors on soft actuators. Different configurations of the printed sensors are studied to investigate how the sensor design affects the performance. The best sensor configuration is selected to provide shape feedback using its changing resistance during deformation. Compared with a commercial flexible bending sensor, the printed sensor has less influences on the soft actuator performance and enjoys higher shape estimation accuracy. Closed loop shape control of the actuator using feedback from the 3D printed sensor is then designed, implemented and compared with the control results using image feedback. A gripper consisting of three individually controlled soft actuators demonstrates the applications of the soft sensor.     

Human-following mobile robot in a distributed intelligent sensor network

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, humans and robots need to be in close proximity to each other as much as possible. Moreover, it is necessary for their interactions to occur naturally. It is desirable for a robot to carry out human following, as one of the human-affinitive movements. The human-following robot requires several techniques: the recognition of the target human, the recognition of the environment around the robot, and the control strategy for following a human stably. In this research, an intelligent environment is used in order to achieve these goals. An intelligent environment is a space in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The control law based on the virtual spring model is proposed to mitigate the difference of movement between the human and the mobile robot. The proposed control law is applied to the intelligent environment and its performance is verified by the computer simulation and the experiment.     

基于云网边端协同计算的智能分发网络研究

智能服务机器人将成为社会发展的重要组成部分和人类工作与生活的重要助手。通过简要介绍由人工智能的云端大脑、基于5G构建的安全神经网络和多关节的机器人本体所组成的云端机器人,提出了基于云、网、边、端协同计算的智能分发网络(Intelligence Distribution Network,IDN)的概念,将IDN应用于云端机器人,以提升云端机器人的智能程度。详细阐述了IDN的架构,从算力、算法、通信、数据、安全等角度对IDN进行了研究,对比分析了智能分发网络与内容分发网络(Content Delivery Network,CDN)的异同点;IDN之于人工智能类似于CDN之于互联网内容,对IDN的典型应用场景进行了测试分析。通过使用IDN,提升了云端机器人智能程度和响应速度、降低了上行带宽和服务成本。尽管当前处于云端机器人的发展初期,但IDN将是实现云端智能机器人大规模商用的必经之路和关键技术,IDN将成为继CDN产业之后的又一个新领域。     

基于菱形光纤布拉格光栅传感阵列的声发射定位技术

声发射技术是结构损伤检测的重要手段,声发射源定位是损伤检测的首要环节。时差定位技术具有快速、高效、精确的特点,以此设计了由菱形阵列光纤布拉格光栅（FBG）传感器构成的声发射定位系统。采用小波变换和传统阈值法提取特征信号,结合互相关法获得传感器间的信号到达时差,然后根据几何定位模型求解非线性方程组得到声源可能存在的位置,最后根据时差的正负特性进一步确定声源的准确位置,有效避免了伪声源的情况。在铝合金板上,以对角线为48 cm48 cm的监测区域进行了10组测试实验验证,平均误差为1.29 cm。     

A Soft Resistive Acoustic Sensor Based on Suspended Standing Nanowire Membranes with Point Crack Design

An artificial basilar membrane (ABM) is an acoustic transducer that mimics the mechanical frequency selectivity of the real basilar membrane, which has the potential to revolutionize current cochlear implant technology. While such ABMs can be potentially realized using piezoelectric, triboelectric, and capacitive transduction methods, it remains notoriously difficult to achieve resistive ABM due to the poor frequency discrimination of resistive‐type materials. Here, a point crack technology on noncracking vertically aligned gold nanowire (V‐AuNW) films is reported, which allows for designing soft acoustic sensors with electric signals in good agreement with vibrometer output—a capability not achieved with corresponding bulk cracking system. The strategy can lead to soft microphones for music recognition comparable to the conventional microphone. Moreover, a soft resistive ABM is demonstrated by integrating eight nanowire‐based sensor strips on a soft trapezoid frame. The wearable ABM exhibits high‐frequency selectivity in the range of 319–1951 Hz and high sensitivity of 0.48–4.26 Pa^?1. The simple yet efficient fabrication in conjunction with programmable crack design indicates the promise of the methodology for a wide range of applications in future wearable voice recognition devices, cochlea implants, and human–machine interfaces.     

机器人光电接近觉传感技术综述

传感技术是智能机器人的关键技术之一。介绍了机器人接近觉传感技术中应用最多的传感器—光电式接近觉传感器的常见三种传感机理和国内外研究现状，并简要地预测未来的发展趋势。     

Tissue-Adhesive Piezoelectric Soft Sensor for In Vivo Blood Pressure Monitoring During Surgical Operation

The reliable function in vivo of self-powered implantable bioelectric devices (iBEDs) requires biocompatible, seamless, effective interactions with biological tissues. Herein, an implantable tissue-adhesive piezoelectric soft sensor (TPSS), in which the piezoelectric sensor converts biomechanical signals into electrical signals, and the adhesive hydrogel (AH) strengthens this conversion by seamlessly adhering the sensor on the wet and curvilinear surface, is proposed. The optimized AH exhibits strong adhesion to various organic or inorganic surfaces, including six commonly used engineering materials and three biological tissues. As a pressure sensor, TPSS proves good in vitro electrical performance with a high output of 8.3 V, long-term stability of over 6000 cycles, and high energy power density of 186.9 µW m⁻². In a large animal experiment, TPSS seamlessly adheres to the right-side internal carotid artery of a Yorkshire pig to monitor blood pressure during a surgical operation. Compared to commercial sensors that work by inserting into tissues, TPSS does not cause any damage and can be peeled off after service. The integration of adhesive hydrogel and self-powered pressure sensors enables biocompatible, seamless, and more efficient interactions between the biological system and iBEDs, which also contributes to next-generation implantable bioelectronics with features of battery-free, intelligent, and accurate.     

A Motion Vector Sensor via Direct‐Current Triboelectric Nanogenerator

Motion vector sensors play an important role in artificial intelligence and internet of things. Here, a triboelectric vector sensor (TVS) based on a direct‐current triboelectric nanogenerator is reported, for self‐powered measuring various motion parameters, including displacement, velocity, acceleration, angular, and angular velocity. Based on the working mechanism of the contact‐electrification effect and electrostatic breakdown, a continuous DC signal can be collected to directly monitor moving objects free from environmental electromagnetic signal interference existing in conventional self‐powered TVSs with an alternative‐current signal output, which not only enhances the sensitivity of sensors, but also provides a simple solution to miniaturize the sensors. Its sensitivity is demonstrated to be equivalent to state‐of‐the‐art photoelectric technology by a comparative experiment in an intelligent mouse. Notably, an intelligent pen based on the miniaturized TVS is designed to realize motion trajectory tracing, mapping, and writing on the curved surface. This work provides a new paradigm shift to design motion vector sensors and self‐powered sensors in artificial intelligent and internet of things.     

Stimuli-Responsive Peptide Self-Assembly to Construct Hydrogels with Actuation and Shape Memory Behaviors

Hydrogel actuators, capable of generating reversible deformation in response to external stimulus, are widely considered as new emerging intelligent materials for applications in soft robots, smart sensors, artificial muscles, and so on. Peptide self-assembly is widely applied in the construction of intelligent hydrogel materials due to their excellent stimulus response. However, hydrogel actuators based on peptide self-assembly are rarely reported and explored. In this study, a pH-responsive peptide (MA-FIID) is designed and introduced into a poly(N-isopropyl acrylamide) backbone (PNIPAM) to construct bilayer and heterogeneous hydrogel actuators based on the assembly and disassembly of peptide molecules under different pH conditions. These peptide-containing hydrogel actuators can perform controllable bending, bucking, and complex deformation under pH stimulation. Meanwhile, the Hofmeister effect of PNIPAM hydrogels endows these peptide-containing hydrogels with enhanced mechanical strength, ionic stimulus response (CaCl₂), and excellent shape-memory property. This work broadens the application of supramolecular self-assembly in the construction of intelligent hydrogels, and also provides new inspirations for peptide self-assembly to construct smart materials.     

一种小型智能割草机器人的设计与实现

针对传统割草机污染高、噪音大和需要人工长时间操作等弊端,结合市场需求,研制了一款小型的可预约工作、自主回充的智能割草机器人。硬件上,系统以STM32为核心,由控制系统、动力系统、传感系统、通信系统和人机交互系统5部分组成;算法和软件上,采用自主回充方案,实现预约模式下机器人多次工作,减少人工操作;为解决机器人在斜坡上无法直线行进问题,设计了一种坡度补偿算法;同时为提高机器人的割草效率,提出了一种自主寻草算法。系统符合国际安全设计指标,经过长时间测试,机器在预约模式、自主回充模式均能稳定可靠的工作。     

Highly Breathable and Stretchable Strain Sensors with Insensitive Response to Pressure and Bending

Wearable tensile strain sensors have aroused substantial attention on account of their exciting applications in rebuilding tactile inputs of human and intelligent robots. Conventional such devices, however, face the dilemma of both sensitive response to pressure and bending stimulations, and poor breathability for wearing comfort. In this paper, a breathable, pressure and bending insensitive strain sensor is reported, which presents fascinating properties including high sensitivity and remarkable linearity (gauge factor of 49.5 in strain 0–100%, R² = 99.5%), wide sensing range (up to 200%), as well as superior permeability to moisture, air, and water vapor. On the other hand, it exhibits negligible response to wide-range pressure (0–100 kPa) and bending (0–75%) inputs. This work provides a new route for achieving wearing comfortable, high-performance, and anti-jamming strain sensors.