RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

巴洛克式古典哀伤 Mariska Karto温存女性人体影像

"巴洛克"作为17世纪初至18世纪流行于欧洲的艺术风格,它崇尚豪华气派和注重表现情感的特点,在时间和空间上的影响都颇为深远。荷兰艺术家Mariska Karto便深中其"毒",她将神秘而哀伤的人体影像融进古典绘画的氛围里,呈现出从古老世界梦境中逃离出来般的画面。源于古典绘画的创作激情Mariska Karto深受欧洲文艺复兴和巴洛克艺术时期古典绘画的影响。

基于ROS的消防机器人激光雷达地图构建方法研究

开发出具有智能感知与路径规划的消防机器人,可以极大地提高消防队的灭火能力,这对减少消防人员伤亡和国家财产损失具有重要意义。因此,如何让消防机器人在火灾现场更加准确有效地构建出一张高精度地图,并通过路径自主规划实现自主控制极为重要。为实现具有智能感知与路径规划的消防机器人,通过搭载Linux系统的ROS机器人系统,开发了基于STM32控制板的消防机器人控制系统。系统具有温度与气体检测功能,并借助激光雷达实现了消防机器人的地图构建。通过对三种建图算法的比较,结果表明在空间较小的环境中Gmapping算法效果较好,而Hector算法和Karto算法可以应对大型环境的地图构建,这对消防机器人地图构建与路径规划研究具有实际意义。

基于2D激光和TOF相机的室内环境3D建图算法

室内环境3D建图技术在移动机器人领域得到了广泛的应用,但由于三维环境可能存在低纹理的情况,建图与定位的精度面临挑战。为此,提出了一种基于2D激光和TOF相机的室内环境3D建图算法,首先,使用Karto SLAM构建2D占用栅格地图;然后,移动机器人再次扫描三维环境,当移动机器人的行走距离差值、偏航角度差值或与上一次重定位时间间隔达到阈值,使用实时相关性扫描匹配算法进行重定位,同时TOF相机采集一帧三维点云数据;最后根据移动机器人系统坐标系的转换关系构建三维点云地图。由于点云地图的存储十分占用内存空间,当机器人位姿估计存在误差时,会导致点云地图明显重叠,将点云地图转换成了OctoMap。所提出的建图算法在不同的环境中进行了实验,并验证了该算法的有效性。