Vision-based Stabilization of Nonholonomic Mobile Robots by Integrating Sliding-mode Control and Adaptive Approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.

Review of Research and Development of Supernumerary Robotic Limbs

Supernumerary robotic limbs(SRLs) are a new type of wearable human auxiliary equipment, which is currently a hot research topic in the world. SRLs have broad applications in many fields, and will provide a reference and technical support for the realization of human-robot collaboration and integration,while playing an important role in improving social security and public services. In this paper, representative SRLs are summarized from the aspects of related literature analysis,research status, ontology structure design, control and driving,sensing and perception, and application fields. This paper also analyzes and summarizes the current technical challenges faced by SRLs, and reviews development progress and key technologies,thus giving a prospect of future technical development trends.

Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

A filter algorithm based on cochlear mechanics and neuron filter mechanism is proposed from the view point of vibration.It helps to solve the problem that the non-linear amplification is rarely considered in studying the auditory filters.A cochlear mechanical transduction model is built to illustrate the audio signals processing procedure in cochlea,and then the neuron filter mechanism is modeled to indirectly obtain the outputs with the cochlear properties of frequency tuning and non-linear amplification.The mathematic description of the proposed algorithm is derived by the two models.The parameter space,the parameter selection rules and the error correction of the proposed algorithm are discussed.The unit impulse responses in the time domain and the frequency domain are simulated and compared to probe into the characteristics of the proposed algorithm.Then a 24-channel filter bank is built based on the proposed algorithm and applied to the enhancements of the audio signals.The experiments and comparisons verify that,the proposed algorithm can effectively divide the audio signals into different frequencies,significantly enhance the high frequency parts,and provide positive impacts on the performance of speech enhancement in different noise environments,especially for the babble noise and the volvo noise.

A Braille Reading System Based on Electrotactile Display With Flexible Electrode Array

Dear editor,Due to visual impairment and lack of social attention,the reading problem of the blind has not been well resolved.Many existing Braille reading devices still have many problems and are not accepted by the public.This paper developed a portable Braille reading system based on electrotactile display technology.The proposed system is composed of a six-channel electrotactile stimulator,a flexible electrode array for Braille display and a graphical user interface(GUI)for monitoring and control.Based on the flexibility of the system,two Braille reading strategies have been designed:spatial mode and sequential mode.The system was preliminary tested by six sighted subjects.The results showed that subjects were able to recognize Braille characters with more than 80%accuracy within less than 10 seconds for the initial use.Compared to existing Braille reading systems,this system is portable,wearable and flexible in control.

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

基于磁驱动正交悬臂探针的三维原子力显微镜技术开发

本文介绍了一种基于磁驱动正交悬臂探针(magnetically driven-orthogonal cantilever probes,MDOCP)的三维原子力显微镜(three-dimensional atomic force microscopy,3D-AFM)表征方法,该方法采用两个独立的三自由度纳米扫描器,能够实现探针沿可控矢量角度跟踪扫描样品表面。该3D-AFM系统还配备了高精度旋转台,可实现360°全向成像。定制的MD-OCP包含水平悬臂、垂直悬臂和磁球三部分,其中磁球可在磁场中机械驱动OCP实现激振。垂直悬臂具有一个突出的尖端,可检测深槽和具有悬垂/凹边特征的结构。首先,对MD-OCP的设计、模拟、制造和性能分析进行了描述;其次,详细介绍了探针振幅补偿和360°旋转原点定位的方法。通过使用标准AFM阶梯光栅进行对比实验,验证了所提出方法对于陡峭侧壁和拐角处细节的表征能力,其中采用了三维地形重建方法将图像整合。通过对具有微梳结构的微机电系统(MEMS)器件进行3D表征,进一步证实了所提出基于MD-OCP的3D-AFM技术的有效性。最后,该技术被用于确定微阵列芯片的关键尺寸(critical dimensions,CD)。实验结果表明,所提出的方法可以高精度地获取三维结构的CD信息,相比于难以获得侧壁信息的二维技术,在三维微纳制造检测领域具有更好的潜力。

An automatic compensation method on compression effect and surface elasticity measurement based on the deflection signal of AFM

Atomic force microscope （AFM）, as an important instrument in micro/nano operation, has been widely used to measure sampie＇s height information. However, the so called compression effect, due to force aroused from the contact of AFM tip with a sample surface, would result in imprecision of the surface＇s height measurement, i.e., the measured height is lower than expected. Up to now, there is not any effective and rapid method to attenuate this kind of measurement error. Thus, in this paper, an algorithm to obtain high accurate height measurement is proposed. Firstly, the concept of force curve is used to analyze the basic principle of the compression effect. Secondly, an automatic compensation method by fusing the height signal and the deflection signal is proposed. The proposed algorithm can also be used to obtain a surface elasticity image. Finally, in order to validate the proposed method, two experiments are conducted with respect to mufti-wall nano-carbon tubes on a silicon substrate and graphemes on a mica substrate.

Permanent Magnet Shape Optimization Method for PMSM Air Gap Flux Density Harmonics Reduction

This paper proposed a permanent magnet optimization method to suppress the air gap flux density harmonic of permanent magnet synchronous motor(PMSM).The method corrected the effective air gap length of the motor,calculated the magnetization length of the permanent in the case of parallel magnetization,and took the influence of the permanent magnet relative permeability into consideration.Based on these works,for a given sinusoidal air gap flux density waveform,the corresponding structural parameters can be calculated,so as to achieve the optimization of the permanent magnet.By using this method to optimize the shape of the magnet,the fundamental wave of the air gap flux density can be retained to the greatest extent,so as to eliminate harmonics and maintain the output capacity at the same time.The feasibility and accuracy of the method have been verified by finite element analysis(FEA)and prototype machine experiment.This method is simple and time-saving,and has a satisfactory accuracy,which provides a reference method for permanent magnet optimization of PMSM.

A Light Space Manipulator with High Load-to-Weight Ratio: System Development and Compliance Control

In order to meet the requirements of the space environment for the lightweight and load capacity of the manipulator,this paper designs a lightweight space manipulator with a weight of 9.23 kg and a load of 2 kg.It adopts the EtherCAT communication protocol and has the characteristics of high load-to-weight ratio.In order to achieve constant force tracking under the condition of unknown environmental parameters,an integral adaptive admittance control method is proposed.The control law is expressed as a third-order linear system equation,the operating environment is equivalent to a spring model,and the control error transfer function is derived.The control performance under the step response is further analyzed.The simulation results show that the proposed integral adaptive admittance control method has better performance than the traditional method.It has no steady-state error,overcomes the problems caused by nonlinear discrete compensation,and can facilitate analysis in the frequency domain,realize parameter optimization,and improve calculation accuracy.

HIT-Hawk and HIT-Phoenix: Two kinds of flapping-wing flying robotic birds with wingspans beyond 2 meters

Inspired by large and medium-sized birds,two kinds of flapping-wing flying robots with wingspans beyond 2 meters were developed.They have the appearance of a hawk and a phoenix respectively,so they are called HIT-Hawk and HIT-Phoenix.In this paper,the bionic concept,theoretical analysis,design and manufacturing are introduced in detail.Firstly,the flight principle and characteristics of large and medium-sized birds were summarized.Then,the aerodynamics was modeled based on the thin airfoil theory,and the main design basis was established.Secondly,the mechanical structures of HIT-Hawk and HIT-Phoenix were designed to ensure the lateral and longitudinal stability and have optimized flight performance.Moreover,an autonomous flight control method was proposed and realized in highly integrated on-onboard controller;it satisfies the strict restrictions on mass,size,power and shape.Finally,the prototypes were fabricated and verified through practical flight experiments.The wingspans of these two flapping wing aircrafts are 2.0 m and 2.3 m respectively,the take-off weights are 1.15 kg and 0.86 kg,and the maximum stable endurance is 65 min(with battery of 3S LiPo,4300 mAh)and 8 min(with battery of 3S LiPo,800 mAh).Their wind resistance can both reach level 4.Compared with the small and micro flapping-wing aerial vehicles that mimic insects or small birds,they both have strong load capacity,strong wind resistance and long endurance.

A Marine Environment Early Warning Algorithm Based on Marine Data Sampled by Multiple Underwater Gliders

This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression(DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube(EWDC) to realize the early warning of a new sampled data file.Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.

Research on point stabilization of a wheeled mobile robot using fuzzy control optimized by GA

A point stabilization scheme of a wheeled mobile robot （WMR） which moves on uneven surface is presented by using tuzzy control. Taking the kinematics and dynamics of the vehicle into account, the fuzzy controller is employed to regulate the robot based on a kinematic nonlinear state feedback control law. Herein, the fuzzy strategy is composed of two velocity control laws which are used to adjust the speed and angular velocity, respectively. Subsequently, genetic algorithm （GA） is applied to optimize the controller parameters. Through the self-optimization, a group of optimum parameters is gotten. Simulation results are presented to show the effectiveness of the control strategy.

Global shape reconstruction of nano grid with singly fixed camera

Depth from defocus(DFD),as a typical shape reconstruction method,has been widely researched in most recent years.However,all the existing DFD algorithms require at least two defocused images with different camera parameters.Unfortunately,in micro/nano manipulation,any change on visual sensor's parameters is absolutely forbidden.Therefore,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper.First,the blurring imaging model is constructed with the relative blurring and the diffusion equation.Second,the relationship between depth and blurring is discussed from four aspects.Subsequently,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm.Finally,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.

A commentary of Remote everything in MIT Technology Review 2021

Remote technology refers to applications that rely on wireless/wired network communication technology to enable the effective interconnection of multiple terminals distributed in different geographical locations,which helps to achieve effective information dissemination and sharing,and thereby improve the convenience and efficiency of remote working and learning.With the rapid development of sensor technology,information and communications technology(ICT),artificial intelligence(AI),and other related technologies,remote technology has made considerable progress since the 1970s and has been widely used in many fields,such as education,medicine.Especially the outbreak of COVID-19,remote technology achieves dramatically development within a short period of time,and leads to big changes in the way of working in many industries and peoples’lifestyles.Therefore,it was selected as one of the“Top 10 Global Breakthrough Technologies”by MIT Technology Review in 2021.

Underwater robot sensing technology:A survey

Underwater robot technologies are crucial for marine resource exploration and autonomous manipulation,and many breakthroughs have been achieved with key indicators(e.g.,dive depth and navigation range).However,due to the complicated underwater environment,the state-of-the-art sensing technologies cannot handle all the needs of underwater observations.To improve the autonomous operating capacity of underwater robots,there is an urgent need to develop underwater sensing technology.Therefore,in this paper,we first introduce the development of underwater robot platforms.We then review some key sensing technologies such as underwater acoustic sensing,underwater optical sensing,underwater magnetic sensing,and underwater bionic sensing.Finally,we point out the challenges of underwater sensing technology and future directions in addressing these challenges,e.g.,underwater bionic sensing,new underwater material development,multisource information fusion,and the construction of general test platforms.

A Navigation Method for Multiple Robots Based on a Single Mobile Node

To satisfy the requirements of independent navigation in low cost multiple robots,a navigation frame of masterslave robots with only one master robot is introduced briefly.A range-only navigation algorithm for slave robots of low cost is presented for the navigation in unknown current.This algorithm only needs a master robot.The master is just a single mobile node which has high navigation performance.Observability of this master-salve system is theoretically analyzed.The conditions of slave robot trajectory under which observability is guaranteed are evaluated by a slave robot in real-time.The slave takes appropriate action to control its heading to achieve observability if observability is lost on the current trajectory.The navigation algorithm is implemented by a standard extended Kalman filter,and it is able to acquire accurate estimation of robot's position.The simulation results show its effectiveness of the proposed algorithm.

Quantum-enhanced reinforcement learning for control:a preliminary study

Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,Internet of Things(IoT),logistics,robotic control,etc.However,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-of between exploration and exploitation.Quantum technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement learning.In this paper,a quantum-enhanced reinforcement learning is pictured for optimal control.In this algorithm,the states and actions of reinforcement learning are quantized by quantum technology.And then,a probability amplifcation method,which can efectively avoid the trade-of between exploration and exploitation via quantized technology,is presented.Finally,the optimal control policy is learnt during the process of reinforcement learning.The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym.The preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-of between exploration and exploitation.The learning performance of this new algorithm is stable with diferent learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.