基于视觉的自适应路径遵循控制器,用于4W移动机器人在各种地形上
Yang Chen1, Wei Zeng2, Xuewu Liu2
1School of Physics and Mechatronics Engineering, Longyan University, Longyan, 364012, Fujian, China. chenyang4117@163.com.
Scientific reports
|December 27, 2025
概括
这项研究介绍了移动机器人的地形适应性路径控制器. 支持矢量机器可以实时适应,改善路径的融合,减少在不同地形上的超越.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机器学习应用 机器学习应用
- 计算机视觉用于机器人技术
背景情况:
- 轮式移动机器人的路径控制是具有挑战性的,因为地形变化会影响方向盘动力学.
- 现有的方法往往难以实时适应不同地形.
- 精确的机器人动态建模对于有效的运动控制至关重要.
研究的目的:
- 为移动机器人开发一种新的路径控制器,能够适应不同的地形.
- 将实时地形识别和动态参数匹配集成到控制系统中.
- 通过最大限度地减少路径错误和收时间来提高机器人导航性能.
主要方法:
- 模拟机器人转向动态作为具有地形特定参数的第一阶段系统.
- 采用支持矢量机 (SVM) 方法来实现即时的图像识别和参数匹配.
- 使用利亚普诺夫稳定理论设计一个随时变化的路径控制器.
- 在可变的地形环境中对线性,方形和圆形路径进行比较实验.
主要成果:
- 与固定参数控制器相比,地形适应控制器对预定义路径的收速度明显更快.
- 在自适应控制器的路径遵循性能中观察到较少的超越.
- 基于SVM的实时参数匹配有效地弥补了地形诱导的动态变化.
结论:
- 拟议的时间变化的参数路径跟踪控制器提高了移动机器人的导航准确性和在不同地形上的效率.
- 使用SVM和图像识别进行实时适应是强大的机器人控制的可行策略.
- 利亚普诺夫稳定理论为设计动态系统的自适应控制器提供了坚实的基础.
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