基于扩展的卡尔曼波器传感器数据融合和扩展的机载摄像头感知,通过行为树控制的非全方位移动机器人的领袖追随者方法
Arpit Joon1, Wojciech Kowalczyk1
1Institute of Automatic Control and Robotics, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, Poland.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究介绍了一种使用内置传感器和ArUco标记器进行精确定位的领先追随者移动机器人控制系统. 增强的感知范围和数据融合提高了机器人的姿势估计,以实现有效的导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 控制系统 控制系统
背景情况:
- 领导者-追随者机器人系统需要强大的本地化来协调任务.
- 机载传感器和视觉标记器对于相对机器人定位至关重要.
- 扩大传感器感知范围可以提高动态环境中的定位精度.
研究的目的:
- 开发和验证一个领导者-追随者移动机器人控制方法.
- 通过旋转的摄像机平台和ArUco标记器来增强机器人本地化.
- 整合传感器数据以改善姿势估计.
主要方法:
- 在旋转平台上使用英特尔RealSense摄像头来扩展感知.
- 实现了ArUco标记和信托地标,用于机器人对机器人和工作空间定位.
- 采用了用于摄像头旋转控制的行为树和用于传感器数据融合的扩展卡尔曼波器 (EKF) (编码器,IMU).
- 整合了机器人操作系统 (ROS) 来实现系统.
主要成果:
- 使用车载传感器和ArUco标记器证明了有效的机器人对机器人和工作空间定位.
- 旋转平台成功地扩大了追随机器人的感知范围.
- 基于EKF的数据融合提高了机器人姿势决定的准确性.
- 实验验证证了拟议的控制算法的有效性.
结论:
- 带有增强感知和数据融合的领导追随者控制方法对移动机器人导航是有效的.
- 旋转摄像头,ArUco标记器和EKF的集成提供了强大的本地化功能.
- 这种方法为协调的多机器人操作提供了可靠的解决方案.
相关概念视频
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A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
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01:26Feedback control systems
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01:22Controller Configurations
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01:30Vector Functions and Motion: Problem Solving
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