强大的模型预测控制与动态的前性重新进入策略,用于差分驱动机器人的轨迹跟踪
Diego Guffanti1, Moisés Filiberto Mora Murillo2,3, Santiago Bustamante Sanchez4
1Centro de Investigación en Mecatrónica y Sistemas Interactivos-MIST, Universidad Indoamérica, Av. Machala y Sabanilla, Quito 170103, Ecuador.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
差速驱动移动机器人的精确轨迹跟踪通过模型预测控制 (MPC) 系统得到了改进. 一种新的重新进入策略确保稳定的路径,即使机器人偏离,提高整体导航的稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 自主导航自主导航自主导航自主导航自主导航自主导航
- 移动机器人的动力学
背景情况:
- 精确的轨迹跟踪是差异驱动移动机器人 (DDMR) 在现实应用中面临的关键挑战.
- 模型预测控制 (MPC) 提供了一个强大的框架,但与显著的路径偏差作斗争.
- 现有的方法缺乏有效的恢复机制,以保持偏差后的稳定跟踪.
研究的目的:
- 实验验证一个MPC控制器集成与一个新的动态前回入战略为四轮DDMR.
- 为了提高DDMR在扰乱条件下的轨迹跟踪的稳定性和准确性.
- 用RMSE和带内百分比等指标量化评估性能改进.
主要方法:
- 实现了一个MPC控制器,对线性 (v) 和角性 (ω) 速度有状态和输入约束.
- 集成了一个SLAM算法与ROS2进行实时测距校正.
- 开发并测试了一个动态的前性重新进入策略,由横向错误激活,插入平稳的恢复轨迹.
主要成果:
- 在名义条件下,最好的MPC配置实现了0.05米的横向RMSE和0.06rad的方向RMSE,68.8%的轨迹在验证带内.
- 在干扰下,拟议的重新入境策略显著提高了稳定性,保持了0.12米的侧向RMSE和51.4%的带内轨迹.
- 重返入境策略的表现优于标准的MPC,因为它使得恢复速度更快,并减少了偏差的大小.
结论:
- 将MPC与拟议的动态前性重返入境战略集成,大大提高了DDMR轨迹跟踪的准确性和稳定性.
- 空间接地恢复机制确保在具有挑战性的场景中保持一致的性能,这对于可靠的航行至关重要.
- 这种结合的方法为在不确定的和动态的环境中可靠的移动机器人导航提供了有希望的解决方案.
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