软体机械臂水下自适应鲁棒视觉伺服

徐璠; 王贺升

doi:10.16383/j.aas.c200457

软体机械臂水下自适应鲁棒视觉伺服

doi: 10.16383/j.aas.c200457

徐璠^1,,
王贺升^1,

1.
上海交通大学电子信息与电气工程学院自动化系上海 200240

基金项目: 国家自然科学基金(62073222, 61722309)资助

详细信息

作者简介:
徐璠：上海交通大学电子信息与电气工程学院自动化系博士研究生. 主要研究方向为软体机器人和视觉伺服. E-mail: xufan_1993@sjtu.edu.cn

王贺升：上海交通大学电子信息与电气工程学院自动化系教授. 主要研究方向为视觉伺服, 服务机器人, 机器人控制, 自动驾驶. 本文通信作者. E-mail: wanghesheng@sjtu.edu.cn

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出版历程
- 收稿日期: 2020-06-24
- 网络出版日期: 2020-12-21
- 刊出日期: 2023-04-20

Adaptive Robust Visual Servoing Control of a Soft Manipulator in Underwater Environment

XU Fan^1
,,
WANG He-Sheng^1
,

1.
Department of Automation, School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240

Funds: Supported by National Natural Science Foundation of China (62073222, 61722309)

More Information

Author Bio:
XU Fan　Ph.D. candidate in the Department of Automation, School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University. Her research interest covers soft robot and visual servoing

WANG He-Sheng　Professor in the Department of Automation, School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University. His research interest covers visual servoing, service robot, robot control, and autonomous driving. Corresponding author of this paper

摘要

摘要: 水下仿生软体机器人在水底环境勘测, 水下生物观测等方面具有极高的应用价值. 为进一步提升仿章鱼臂软体机器人在特殊水下环境中控制效果, 提出一种自适应鲁棒视觉伺服控制方法, 实现其在干扰无标定环境中的高精度镇定控制. 基于水底动力学模型, 设计保证动力学稳定的控制器; 针对柔性材料离线标定过程繁琐、成本高, 提出材料参数自适应估计算法; 针对水下特殊工作条件, 设计自适应鲁棒视觉伺服控制器, 实现折射效应的在线补偿, 并通过自适应未知环境干扰上界, 避免先验环境信息的求解. 所提算法在软体机器人样机中验证其镇定控制性能, 为仿生软体机器人的实际应用提供理论基础.
- 软体机器人 /
- 自适应鲁棒控制 /
- 无标定视觉伺服 /
- 水下视觉伺服
Abstract: Underwater bioinspired soft robots enjoy high applicability in underwater exploring, biology observing, etc. This paper proposes an adaptive robust controller to improve the performance of the octopus-inspired soft robot in underwater environment and to realize accurate positioning control in the uncalibrated environment with disturbances. The paper designed a dynamically stable controller based on the underwater dynamic model. Considering the high-costing and tedious offline identification of soft material parameters, the paper proposed an adaptive method to estimate the unknowns. Considering the special underwater working condition, the paper proposed an adaptive robust visual servo controller, which can online compensate for refraction effect and avoid solving prior environment information by estimating unknown upper bound of environment disturbances. The proposed algorithm was experimentally validated in a soft robot prototype, serving as a theoretical basis of applications of bioinspired soft robots.
- Soft robot /
- adaptive robust control /
- uncalibrated visual servoing /
- underwater visual servoing

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图 1 原型样机简图

Fig. 1 Depiction of the prototype

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图 2 水下相机模型

Fig. 2 Underwater camera model

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图 3 控制器简图

Fig. 3 Block diagram of the controller

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图 4 实验设置

Fig. 4 Experiment setup

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图 5 图像误差收敛过程曲线

Fig. 5 Converging process of image error

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图 6 图像轨迹曲线

Fig. 6 Image trajectory curve

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图 7 3D轨迹曲线

Fig. 7 3D trajectory curve

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图 8 ${\hat {\boldsymbol{\chi}} _D}$中未知参数$\hat E$和未知干扰上界$\hat \theta $的收敛过程曲线

Fig. 8 Converging process of estimated parameters $\hat E $ in ${\hat {\boldsymbol{\chi}} _D}$ and of unknown interference upper bound $\hat \theta $

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图 9 ${\hat {\boldsymbol{\chi}} _C}$中未知参数的收敛过程曲线

Fig. 9 Converging process of estimated parameters in ${\hat {\boldsymbol{\chi}} _C}$

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参考文献(24)

[1]	Rus D, Tolley MT. Design, fabrication and control of soft robots. Nature 2015, 521(7553): 467-475.
[2]	Gong Z Y, Fang X, Chen X Y, Cheng J H, Xie Z X, Liu J Q, et al. A soft manipulator for efficient delicate grasping in shallow water: Modeling, control, and real-world experiments. The International Journal of Robotics Research, 2021, 40(1): 449−469
[3]	Till J, Aloi V, Rucker C. Real-time dynamics of soft and continuum robots based on Cosserat rod models. The International Journal of Robotics Research 2019, 38(6): 723-746.
[4]	Qiao H, Li QS, Li GQ. Vibratory characteristics of flexural non-uniform Euler–Bernoulli beams carrying an arbitrary number of spring–mass systems. International Journal of Mechanical Sciences 2002, 44(4): 725-743.
[5]	Jones BA, Walker ID. Kinematics for multisection continuum robots. IEEE Transactions on Robotics 2006, 22(1): 43-55.
[6]	Allen T F, Rupert L, Duggan T R, Hein G, Albert K. Closed-form non-singular constant-curvature continuum manipulator kinematics. In: Proceedings of the 3rd IEEE International Conference on Soft Robotics (RoboSoft). New Haven, CT, USA: IEEE, 2020. 410−416
[7]	Grazioso S, Di Gironimo G, Siciliano B. A Geometrically Exact Model for Soft Continuum Robots: The Finite Element Deformation Space Formulation. Soft Robotics 2018, 6(6): 790-811.
[8]	Thuruthel TG, Falotico E, Renda F, Laschi C. Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators. IEEE Transactions on Robotics 2019, 35(1): 124-134.
[9]	Xu F, Wang H, Au KWS, Chen W, Miao Y. Underwater Dynamic Modeling for a Cable-Driven Soft Robot Arm. IEEE/ASME Transactions on Mechatronics 2018, 23(6): 2726-2738.
[10]	Han L J, Wang H S, Liu Z, Chen W D, Zhang X F. Vision-based cutting control of deformable objects with surface tracking. IEEE/ASME Transactions on Mechatronics, 2021, 26(4): 2016−2026
[11]	Qiao H, Li Y, Tang T, Wang P. Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model. IEEE Trans Cybern 2014, 44(9): 1485-1496.
[12]	徐德. 单目视觉伺服研究综述. 自动化学报 2018, 44(10): 1729-1746. Xu De. A tutorial for monocular visual servoing. Acta Automatica Sinica, 2018, 44(10): 1729-1746.
[13]	Xu F, Wang H, Liu Z, Chen W. Adaptive visual servoing for an underwater soft robot considering refraction effects. IEEE Transactions on Industrial Electronics 2020, 67(12): 10575-10586.
[14]	Gu H Y, Wang H S, Xu F, Liu Z, Chen W D. Active fault detection of soft manipulator in visual servoing. IEEE Transactions on Industrial Electronics, 2021, 68(10): 9778−9788
[15]	Liu YH, Wang H, Wang C, Lam KK. Uncalibrated visual servoing of robots using a depth-independent interaction matrix. IEEE Transactions on Robotics 2006, 22(4): 804-817.
[16]	Bonkovic M, Hace A, Jezernik K. Population-based uncalibrated visual servoing. IEEE/ASME Transactions on Mechatronics 2008, 13(3): 393-397.
[17]	Li M, Kang R, Branson DT, Dai JS. Model-free control for continuum robots based on an adaptive kalman filter. IEEE/ASME Transactions on Mechatronics 2018, 23(1): 286-297.
[18]	Meline A, Triboulet J, Jouvencel B. A camcorder for 3D underwater reconstruction of archeological objects. In: Proceedings of IEEE Oceans 2010 MTS/IEEE Seattle. Seattle, USA: IEEE, 2010. 1−9
[19]	Botelho S S da C, Drews P, Oliveira G L, Figueiredo M da S. Visual odometry and mapping for underwater autonomous vehicles. In: Proceedings of the 6th Latin American Robotics Symposium. Valparaiso, Chile: IEEE, 2009. 1−6
[20]	Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation 1987, 3(4): 323-344.
[21]	Traffelet L, Eppenberger T, Millane A, Schneider T, Siegwart R. Target-based calibration of underwater camera housing parameters. In: Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Lausanne, Switzerland: IEEE, 2016. 201−206
[22]	Zhao S, Li X J, Liu T. Camera distortion calibration method based on nonspecific planar target. In: Proceedings of the IEEE International Conference on Signal and Image Processing (ICSIP). Beijing, China: IEEE, 2016. 452−457
[23]	He W, Ge SS, Zhang S. Adaptive boundary control of a flexible marine installation system. Automatica 2011, 47(12): 2728-2734.
[24]	Forsyth D A, Ponce J. Computer Vision: A Modern Approach. London: Pearson Education, 2002.