A viscoelastic nonlinear energy sink with an electromagnetic energy harvester:Narrow-band random response

Nonlinear energy sink is a passive energy absorption device that surpasses linear dampers, and has gained significant attention in various fields of vibration suppression. This is owing to its capacity to offer high vibration attenuation and robustness across a wide frequency spectrum. Energy harvester is a device employed to convert kinetic energy into usable electric energy. In this paper, we propose an electromagnetic energy harvester enhanced viscoelastic nonlinear energy sink(VNES) to achieve passive vibration suppression and energy harvesting simultaneously. A critical departure from prior studies is the investigation of the stochastic P-bifurcation of the electromechanically coupled VNES system under narrowband random excitation. Initially, approximate analytical solutions are derived using a combination of a multiple-scale method and a perturbation approach. The substantial agreement between theoretical analysis solutions and numerical solutions obtained from Monte Carlo simulation underscores the method's high degree of validity. Furthermore, the effects of system parameters on system responses are carefully examined. Additionally, we demonstrate that stochastic P-bifurcation can be induced by system parameters, which is further verified by the steady-state density functions of displacement. Lastly,we analyze the impacts of various parameters on the mean square current and the mean output power, which are crucial for selecting suitable parameters to enhance the energy harvesting performance.

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting efficiency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in clutter.To effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in clutter.Specifically,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping operations.In addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered environments.To further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end methods.Noted that the authors’system can be robustly applied to real-world use and extended to novel objects.Supplementary material is available at https://youtu.be/NhG\_k5v3NnM}{https://youtu.be/NhG\_k5v3NnM.

Autonomous landing scene recognition based on transfer learning for drones

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network(CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum(ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent(SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7dataset, paving the way for drones to autonomously learn landing scenes.

Scene image recognition with knowledge transfer for drone navigation

In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot

For improving the estimation accuracy and the convergence speed of the unscented Kalman filter(UKF),a novel adaptive filter method is proposed.The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function.On the basis of the MIT rule,an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function.The updated covariance is fed back into the normal UKF.Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations.The asymptotic properties of this adaptive UKF are discussed.Simulations are conducted using an omni-directional mobile robot,and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.

ANALYSIS OF TIPOVER STABILITY FOR NOVEL SHAPE SHIFTING MODULAR ROBOT

A novel three-module robot has been introduced. It can change its configuration to adapt to the uneven terrain and to improve its tipover stability. This three-module tracked robot has three kinds of symmetry configuration. They are line type, triangle type, and row type. After the factors and the countermeasures of mobile robot＇s tipover problem are analyzed, stability pyramid and tipover stabil-ity index are proposed to globally determinate the mobile robot＇s static stability and dynamic stability. The shape shifting robot is tested by this technique under the combined disturbance of pitch, roll and yaw in simulation. The simulation result shows that this technique is effective for the analysis of mobile robot＇s tipover stability, especially for the reconfigurable or shape shifting modular robot. Experiments on three symmetry configurations are made under unstructured environments. The environment experiment shows the same result as that of the simulation that the triangle type configuration has the best stability. Both simulation and experiment provide a valid reference for the reconfigurable robot＇s potential application.

DEVELOPMENT OF A SHAPE-SHIFTING MOBILE ROBOT FOR URBAN SEARCH AND RESCUE

A portable shape-shifting mobile robot system named as Amoeba Ⅱ（A-Ⅱ） is developed for the urban search and rescue application. It is designed with three degrees of freedom and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. And the joint unit can transform the robot shape to get high environment adaptation. A-Ⅱ robot can not only adapt to the environment but also change its body shape according to the locus space. It behaves two work states including the linear state （named as I state） and the parallel state （named as Ⅱ state）. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave, etc. The parallel state enables the robot with high mobility on rough ground. Also, the joint unit can propel the robot to roll in sidewise direction. Two modular A-Ⅱ robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. The experimental results validate the adaptation and mobility of A-Ⅱ robot.

NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP

In order to achieve precise,robust autonomous guidance and control of a tracked vehicle,a kinematic model with longitudinal and lateral slip is established,Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly.The first filter is the well-known extended Kalman filter.The second filter is an unscented version of the Kalman filter.The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution.The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies.The four different approaches have different complexities,behavior and advantages that are surveyed and compared.

Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone

With the increasing use of humanoid robots in several sectors of industrial automation and manufacturing, navigation and path planning of humanoids has emerged as one of the most promising area of research. In this paper, a navigational controller has been developed for a humanoid by using fuzzy logic as an intelligent algorithm for avoiding the obstacles present in the environment and reach the desired target position safely. Here, the controller has been designed by careful consideration of the navigational parameters by the help of fuzzy rules. The sensory information regarding obstacle distances and bearing angle towards the target are considered as inputs to the controller and necessary velocities for avoiding the obstacles are obtained as outputs. The working of the controller has been tested on a NAO humanoid robot in V-REP simulation platform. To validate the simulation results, an experimental platform has been designed under laboratory conditions, and experimental analysis has been performed.Finally, the results obtained from both the environments are compared against each other with a good agreement between them.

Robust Leader-follower Formation Control of Mobile Robots Based on a Second Order Kinematics Model

在这份报纸，我们学习为活动机器人的领导人追随者形成建模并且控制的问题。首先，为领导人追随者机器人形成的一个新奇 kinematics 模型基于在机器人和追随者机器人的本地运动之间的相对运动状态被提出。用这个模型，向心的亲戚和在机器人之间的 Coriolis 加速被测量相对、本地的运动传感器直接计算，并且利用了线性化非线性的系统方程。一个形成控制器，由反馈 linearization 部分和一个滑动模式赔偿者组成，被设计包括内部动力学稳定全面系统。控制获得被解决坚韧性不平等决定并且假定满足保证形成系统的零动力学的稳定性的一个合作协议。建议控制器为追随者机器人产生命令的加速并且使形成控制系统柔韧到领导人机器人的无节制的加速的效果。而且，一个柔韧的适应控制器被开发在系统处理参量的无常。模拟和试验性的结果表明了建议控制方法的有效性。

ONLINE MODEL AND ACTUATOR FAULT TOLERANT CONTROL FOR AUTONOMOUS MOBILE ROBOT

A novel fault-tolerant adaptive control methodology against the actuator faults is proposed. The actuator effectiveness factors （AEFs） are introduced to denote the healthy of actuator, and the unscented Kalman filter （UKF） is employed for online estimation of both the motion states and the AEFs of mobile robot. A square root version of the UKF is introduced to improve efficiency and numerical stability. Using the information from the UKF, the reconfigurable controller is designed automatically based on an enhancement inverse dynamic control （IDC） methodology. The experiment on a 3-DOF omni-directional mobile robot is performed, and the effectiveness of the proposed method is demonstrated.

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

Multiobjective Optimization for Controller Design

用 multiobjective 优化方法的控制器设计被考虑，在哪个为设计控制器的目的和限制被分析并且改善。以便同时地满足目的，一个新 multiobjective 优化算法被介绍进设计一个最佳的 PID 控制器，由织物 P 系统启发了。控制器的参数根据联系的膜的规则被编码并且演变。唯一的设计是结构的整个人口被划分成几 subpopulations 减少计算复杂性。模拟结果证明算法快收敛，答案形成精确前面并且一致地散布。P 系统的变体设计的控制器有令人满意的性能。而且，答案的分析证明新算法对学习在性能之间的关系并且调节参数合适。建议方法为设计并且评估不同控制器是有用的。

Two-dimensional PSD based automatic docking of self-reconfiguration modular exploration robot system

Based on the design of a docking mechanism,this paper thoroughly investigates the space automatic doc- king of self-reconfiguration modular exploration robot system(RMERS).The method that leads robot to achieve space docking by using two-dimensional PSD is put forward innovatively for the median size robot system.At the same time,in order to enlarge the detecting extension and the precision of PSD and reduce its dependence on light- ing signal,the PSD was remade by increasing the optical device over its light-sensitive surface.The emission board and LED light scheduling were designed according to docking arithmetic,and the operating principle of docking process was analyzed based on these.The simulation experiments were carried out and their results are presented.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Design and optimization of a semi-active suspension system for railway applications

The present work focused on the application of innovative damping technologies in order to improve railway vehicle performances in terms of dynamic stability and comfort. As a benchmark case-study, the secondary sus- pension stage was selected and different control techniques were investigated, such as skyhook, dynamic compensation, and sliding mode control. The final aim was to investigate which control schemes are suitable for optimal exploitation of the non-linear behavior of the actuators. The performance improvement achieved by adoption of the semi-active dampers on a standard high-speed train was evaluated in terms of passenger comfort. Different control strategies have been investigated by comparing a simple SISO （single input single output） regulator based on the skyhook damper ap- proach with a centralized regulator. The centralized regulator allows for the estimation of a near optimal set of control forces that minimize car-body accelerations with respect to constraints imposed by limited performance of semi-active actuators. Simulation results show that best results is obtained using a mixed approach that considers the simultaneous applications of model based and feedback compensation control terms.

Formation Control of Mobile Robots with Active Obstacle Avoidance

在这份报纸，形成控制和障碍回避问题被处理一统一了控制算法，它允许追随者当维持从领导人的需要的相对适用或相对距离时，避免障碍。In the known 领导人追随者机器人形成控制文学，领导人机器人的绝对运动状态被要求控制追随者，它不能在一些环境是可得到的。在这研究，领导人追随者机器人形成以在领导人和追随者机器人之间的相对运动状态被建模并且控制。领导人机器人的绝对运动状态没在建议形成控制器被要求。而且，研究基于察觉到在机器人和障碍之间的相对运动被扩大了到一个新奇障碍回避计划。试验性的调查用平台被进行了由活动机器人和计算机视觉系统，和结果表明了的三 nonholonomic 组成了建议方法的有效性。

CONTINUATION METHOD APPLIED IN KINEMATICS OF PARALLEL ROBOT

Continuation method solving forward kinematics problem of parallel robot was discussed. And through a coefficient-parameter continuation method the efficiency and feasibility of continuation method were improved. Using this method all forward solutions of a new parallel robot model which was put forward lately by Robot Open Laboratory of Science Institute of China were obtained. Therefore it provided the basis of mechanism analysis and real-time control for new model.

A new accelerating algorithm for multi-agent reinforcement learning

In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.