Robot programming by demonstration: a novel system for robot trajectory programming based on robot operating system

In this article,a new trajectory programming system that allows non-expert users to intuitively and efficiently program trajectories for robots is proposed.The system tracks a pen-shaped marker and obtains its position and orientation by processing the point cloud data of the workspace.A graphical user interface,which enables users to save and execute the acquired trajectory immediately after performing trajectory demonstration,is designed and developed for the system.The performance of the developed system is experimentally evaluated by using it to program trajectories for a UR5 robot.The results indicate that compared with traditional kinesthetic programming,the developed system has the potential of significantly reducing the ergonomic stress and workload of users.The system is developed based on the robot operating system,which facilitates its integration with different robot control systems.

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

SMINER:Detecting Unrestricted and Misimplemented Behaviors of Software Systems Based on Unit Test Cases

Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage.Therefore,it is an essential task to discover unrestricted and misimplemented behaviors of a system.However,it is a daunting task for security experts to discover such vulnerabilities in advance because it is timeconsuming and error-prone to analyze the whole code in detail.Also,most of the existing vulnerability detection approaches still focus on detecting memory corruption bugs because these bugs are the dominant root cause of software vulnerabilities.This paper proposes SMINER,a novel approach that discovers vulnerabilities caused by unrestricted and misimplemented behaviors.SMINER first collects unit test cases for the target system from the official repository.Next,preprocess the collected code fragments.SMINER uses pre-processed data to show the security policies that can occur on the target system and creates a test case for security policy testing.To demonstrate the effectiveness of SMINER,this paper evaluates SMINER against Robot Operating System(ROS),a real-world system used for intelligent robots in Amazon and controlling satellites in National Aeronautics and Space Administration(NASA).From the evaluation,we discovered two real-world vulnerabilities in ROS.

面向任务的无人智能清扫车系统设计

近年来,自主清扫系统,例如机器人吸尘器,因其能够自主导航和清洁室内空间的能力,越来越受到社会的欢迎。然而,这些系统的应用仍受限于其清洁范围有限、无法适应各种环境等问题。本研究旨在解决这些局限性,通过提出一种基于ROS2框架的面向任务的智能清扫车系统。该系统利用ROS2的模块化、灵活性和进程间通信等优势,以增强清扫系统的性能和适应性。主要目标是开发一种能够高效、有效地清洁室内空间的系统。研究的关键步骤包括对自主清扫系统和ROS2的文献进行分析,设计和开发硬件架构,实现ROS2软件组件,以及对系统的性能进行严格的测试和评估。

基于激光雷达的停车场车辆定位算法

在停车场、隧道中GPS、Wi-Fi信号受限的情况下,提出一种基于激光雷达的车辆自主定位方法。采用激光雷达SLAM(simultaneous localization and mapping)算法,通过三维激光雷达点云匹配获取车辆的估计位姿;根据图优化方法和非线性优化方法,对所有位姿进行后端调整,进而得到分辨率可控的环境信息平面栅格地图;基于蒙特卡洛方法,采用粒子滤波器进行实时车辆定位,并提出了粒子采样的一种改善方式,实现了较高精度的激光雷达自主定位。结果表明:粒子滤波器能够有效地实现车辆在停车场等无GPS环境下的定位,定位精度在10 cm之内。

基于改进A^(*)与DWA算法的物流机器人路径规划

传统的仓库自动导引车(automated guided vehicle,AGV)一般按照铺设的磁轨道进行作业,导致机器人搬运路线并非最优路线,工作效率较低。研究了一种基于机器人操作系统(robot operating system,ROS)的路径规划自主控制算法,从实际应用场景出发,对移动机器人运行的仓储环境利用同时定位与地图创建(simultaneous localization and mapping,SLAM)构建地图,对传统的A*算法和动态窗口法(dynamic window approach,DWA)算法进行改进优化,并在ROS开发平台上开发程序,进行实验验证。实验结果表明,该方法能有效实现自主导航、动态避障功能以及路径优化,并从路径转折次数、运行总时间、运行总路径长度等角度验证了策略的有效性。

Development of a Robotic Cochlear Implantation System

Traditional cochlear implantation surgery has problems such as high surgical accuracy requirement and large trauma,which cause the difficulty of the operation and the high requirements for doctors,so that only a few doctors can complete the operation independently.However,there is no research on robotic cochlear implantation in China.In response to this problem,a robotic cochlear implantation system is proposed.The robot is controlled by robot operating system(ROS).A simulation environment for the overall surgery is established on the ROS based on the real surgery environment.Through the analysis of the kinematics and the motion planning algorithm of the manipulator,an appropriate motion mode is designed to control the motion of the manipulator,and perform the surgery under the simulation environment.A simple and feasible method of navigation is proposed,and through the model experiment,the feasibility of robotic cochlear implantation surgery is verified.

Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

With the continuous development of robotics and artificial intelligence,robots are being increasingly used in various applications.For traditional navigation algorithms,such as Dijkstra and A*,many dynamic scenarios in life are difficult to cope with.To solve the navigation problem of complex dynamic scenes,we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present.The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output.The algorithm enhances the robots’ability to actively avoid obstacles while retaining the adaptability of traditional methods.

A pre-generated matrix-based method for real-time robotic drilling chatter monitoring

Currently, due to the detrimental effects on surface finish and machining system, chatter has been one crucial factor restricting robotic drilling operations, which improve both quality and efficiency of aviation manufacturing. Based on the matrix notch filter and fast wavelet packet decomposition, this paper presents a novel pre-generated matrix-based real-time chatter monitoring method for robotic drilling. Taking vibration characteristics of robotic drilling into account, the matrix notch filter is designed to eliminate the interference of spindle-related components on the measured vibration signal. Then, the fast wavelet packet decomposition is presented to decompose the filtered signal into several equidistant frequency bands, and the energy of each sub-band is obtained. Finally, the energy entropy which characterizes inhomogeneity of energy distribution is utilized as the feature to recognize chatter on-line, and the effectiveness of the presented algorithm is validated by extensive experimental data. The results show that the proposed algorithm can effectively detect chatter before it is fully developed. Moreover, since both filtering and decomposition of signal are implemented by the pre-generated matrices, calculation for an energy entropy of vibration signal with 512 samples takes only about 0.690 ms. Consequently, the proposed method achieves real-time chatter monitoring for robotic drilling, which is essential for subsequent chatter suppression.

Research on Visual Autonomous Navigation Indoor for Unmanned Aerial Vehicle

The aim of this paper is to study visual autonomous navigation of unmanned aerial vehicle(UAV) in indoor global positioning system(GPS) denied environment. The UAV platform of the autonomous navigation flight control system is designed and built. The principle of visual localization and mapping algorithm is studied.According to the characteristics of UAV platform, the visual localization is designed and improved. Experimental results demonstrate that the UAV platform can realize the tasks of autonomous localization, navigation and mapping based on visual in unknown environments.