| 基于视觉SLAM–伺服框架的移动机器人指令滤波反步控制 |
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| 引用本文： | 李晨萍,张雪波,王润花,李宝全,方勇纯. 基于视觉SLAM–伺服框架的移动机器人指令滤波反步控制[J]. 控制理论与应用, 2022, 39(12): 2233-2241 |
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| 作者姓名： | 李晨萍 张雪波 王润花 李宝全 方勇纯 |
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| 作者单位： | 南开大学人工智能学院机器人与信息自动化研究所,南开大学人工智能学院机器人与信息自动化研究所,南开大学人工智能学院机器人与信息自动化研究所,天津工业大学电气工程与自动化学院,南开大学人工智能学院机器人与信息自动化研究所 |
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| 基金项目： | 国家重点研发计划课题(2018YFB1307503), 天津市杰出青年基金项目(19JCJQJC62100), 天津市自然科学基金面上项目(19JCYBJC18500), 中央 高校基本科研业务费项目资助. |
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| 摘 要： | 针对移动机器人位姿镇定问题, 本文提出基于视觉同时定位与建图(simultaneous localization and mapping, SLAM)–伺服框架的指令滤波反步控制策略. 具体而言, 通过加速度层控制器设计进而积分得到的光滑速度信号, 减小SLAM视觉模块的预测位姿误差; 继而应用指令滤波器简化控制器设计的复杂求导运算, 减轻计算负担; 此外, SLAM模块利用运动信息与视觉信息的融合解决未知尺度问题, 降低未知深度造成的控制器设计复杂度. 通过李雅普诺夫理论可以证明闭环系统的稳定性. 仿真和实验结果最终验证了本文算法的有效性.
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| 关 键 词： | 指令滤波 速度光滑 视觉伺服 同时定位与建图 移动机器人 |
| 收稿时间： | 2021-12-23 |
| 修稿时间： | 2022-11-18 |
Command filter backstepping control for mobile robots based on visual SLAM and servoing framework |
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| LI Chen-ping,ZHANG Xue-bo,WANG Run-hu,LI Bao-quan and FANG Yong-chun. Command filter backstepping control for mobile robots based on visual SLAM and servoing framework[J]. Control Theory & Applications, 2022, 39(12): 2233-2241 |
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| Authors: | LI Chen-ping ZHANG Xue-bo WANG Run-hu LI Bao-quan FANG Yong-chun |
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| Affiliation: | Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University,Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University,Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University,School of Electrical Engineering and Automation, Tianjin Polytechnic University,Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University |
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| Abstract: | For the pose regulation task of mobile robots, a command filter backstepping controller based on visual SLAM and servoing framework is proposed in this paper. Specifically, acceleration signals are designed and then integrals are computed to obtain smooth velocity commands, so that error of predicted pose can be reduced in the SLAM module. Then a command filter is introduced to simplify the computation process of derivation terms. Additionally, velocities designed by the controller are adopted in the SLAM module for scale estimation, thus reducing the complexity of controller design resulted from unknown depth. Stability of the closed-loop system is analyzed by Lyapunov-based techniques. Finally, simulation and experiments are implemented to verify the effectiveness of the proposed visual servoing controller. |
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| Keywords: | command filter smooth velocities visual servoing SLAM mobile robots |
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