基于SLAM的全向移动机器人避障研究

V. Nandikolla, Bryan Ghoslin
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摘要

在移动机器人领域,同时定位和映射(SLAM)是一种算法方法,用于创建和更新环境地图,同时跟踪机器人在环境中的位置。SLAM算法的应用对于自主移动系统在穿越环境的同时避开障碍物并准确到达指定的目标目的地非常重要。介绍了一种半自主全向移动机器人slam驱动控制器的设计。系统的输入来自脑机接口,以简单的驾驶命令或用户决定的目标目的地的形式。由于实时反应和响应的延迟问题,系统必须按照最后给定的命令安全地导航,直到耗尽可用空间、达到目标指定或接收到新命令。该机器人系统利用一个带有升级传感器系统的三轮机器人套件。英特尔RealSense深度相机D435和两个激光雷达传感器被用来构建一个完整的360°视野。利用机器人操作系统(ROS)开发了SLAM算法和系统控制器。控制器是在Gazebo中开发和测试的,Gazebo是一个用于快速原型设计的物理模拟引擎。测试是为了验证控制器在给定不同命令时的性能,以及执行长距离路径规划和避障。系统通常能够以大约3%或更小的误差实现其目标目的地,尽管发现错误随着系统处理的命令越多而增加。
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Obstacle Avoidance for Omnidirectional Mobile Robot Using SLAM
In the field of mobile robotics, Simultaneous Localization and Mapping (SLAM) is an algorithmic approach to the computational problem of creating and updating a map of an environment while simultaneously keeping track of where the robot is within the environment. Applications of a SLAM algorithm are important for autonomous mobile systems to traverse an environment while avoiding obstacles and accurately achieving designated goal destinations. This paper presents the design of a SLAM-driven controller for a semi-autonomous omnidirectional mobile robot. Input to the system comes from a Brain Computer Interface in the form of simple driving commands or a goal destination as decided by the user. Due to latency issues of reacting and responding in real time, the system must safely navigate following the last given commands until it runs out of free space, reaches a goal designation, or receives a new command. The robotic system utilizes a three-wheeled robot kit with an upgraded sensor system. The Intel RealSense Depth Camera D435 and two lidar sensors are utilized to construct a full 360° field of view. The SLAM algorithm and system controllers are developed using the Robot Operating System (ROS). The controllers are developed and tested within Gazebo, which is a physics simulation engine utilized for rapid prototyping. Testing was performed to validate controller performance when given varying commands as well as performing long distance path planning and obstacle avoidance. The system was often capable of achieving its goal destinations with a small error of around 3% or less though the error was found to increase with the more commands the system processed.
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