基于视觉感知的海生物吸纳式水下机器人目标捕获控制
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摘要
针对近海水产养殖环境下海生物目标的机器捕捞,设计了一款海生物吸纳式水下机器人.该水下机器人可以采取手动遥控吸取和视觉伺服控制吸取方式完成对海生物目标的机器捕捞.为了实现基于视觉感知的目标捕获控制,在摄像机平面坐标系上建立了水下机器人和目标之间的运动学关系,并在此基础上提出了自适应递归神经网络控制器.通过设计递归神经网络估计和补偿外界环境干扰,利用S面函数使水下机器人快速到达期望位置并保持稳定,结合递归神经网络和系统动力学模型设计鲁棒函数进一步提高非线性系统在视觉控制中的可靠性和稳定性.最后,在近海自然养殖条件下对海生物进行视觉跟踪控制实验,实现了对海生物目标的主动吸取控制,验证了该控制器的功能.
For the machinery capture of sea organism targets in the offshore aquaculture, a sea organism absorptive underwater vehicle is designed. The underwater vehicle can absorb and capture sea organism targets in manual tele-operation mode and visual servo mode. In order to realize vision based target capture control, the kinematic relationship between the underwater vehicle and the target is established in the camera plane coordinate, and the adaptive recurrent neural network controller is proposed on this basis. The recurrent neural network is designed to estimate and compensate environmental disturbance. The S surface function is utilized to make the underwater vehicle reach the expected position quickly and remain stable. Moreover, a robust function on the basis of recurrent neural network and system dynamic model is designed to improve the reliability and stability of the nonlinear system in visual control. Finally, a visual tracking control experiment for sea organisms is conducted in the offshore aquaculture, and active absorption control of sea organism targets is achieved,which verifies the controller function.
For the machinery capture of sea organism targets in the offshore aquaculture, a sea organism absorptive underwater vehicle is designed. The underwater vehicle can absorb and capture sea organism targets in manual tele-operation mode and visual servo mode. In order to realize vision based target capture control, the kinematic relationship between the underwater vehicle and the target is established in the camera plane coordinate, and the adaptive recurrent neural network controller is proposed on this basis. The recurrent neural network is designed to estimate and compensate environmental disturbance. The S surface function is utilized to make the underwater vehicle reach the expected position quickly and remain stable. Moreover, a robust function on the basis of recurrent neural network and system dynamic model is designed to improve the reliability and stability of the nonlinear system in visual control. Finally, a visual tracking control experiment for sea organisms is conducted in the offshore aquaculture, and active absorption control of sea organism targets is achieved,which verifies the controller function.
引文
[1]Geoff K. Trip report for the November 2014 marketing mission to China[R/OL]//Explorations Unlimited Inc Brentwood Bay,BC.(2014-11-5)[2018-5-18]. http://www.pscha.org.
[2]封锡盛,李一平.海洋机器人30年[J].科学通报,2013,58(zII):2-7.Feng X S, Li Y P. Marine robot for 30 years[J]. Chinese Science Bulletin, 2013, 58(zII):2-7.
[3]Hordur J, Michael K, Brendan E, et al. Imaging sonar-aided navigation for autonomous underwater harbor surveillance[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2010:4936-4943.
[4]曾俊宝,李硕,李一平,等.便携式自主水下机器人控制系统研究与应用[J].机器人,2016,38(1):91-97.Zeng J B, Li S, Li Y P, et al. Research and application of the control system for a portable autonomous underwater vehicle[J].Robot, 2016, 38(1):91-97.
[5]Pere R, Marc C, David R, et al. Intervention AUVs:The next challenge[J]. Annual Reviews in Control, 2015, 40(1):227-241.
[6]刘泽发.观测型ROV航行控制系统设计及运动控制技术研究[D].杭州:浙江大学,2015.Liu Z F. Development of navigation control system and research on the motion control technique of observation-class ROV[D].Hangzhou:Zhejiang University, 2015.
[7]Oussama K, Yeh X, Gerald B, et al. Ocean One:A robotic avatar for oceanic discovery[J]. IEEE Robotics&Automation Magazine, 2016, 23(4):20-29.
[8]Cork P. Robotics, vision and control[M]. Berlin, Germany:Springer, 2014.
[9]Antonio C L, Fernando L. Passivity-based adaptive 3D visual servoing without depth and image velocity measurements for uncertain robot manipulators[J]. International Journal of Adaptive Control and Signal Processing, 2016, 30(8/9/10):1269-1297.
[10]Mohebbi A, Keshmiri M, Xie W. A comparative study of eye-in-hand image-based visual servoing:Stereo vs. mono[J]. Journal of Integrated Design and Process Science, 2015,19(3):25-54.
[11]刘鹏玉.服务机器人手眼协调仿生控制研究[D].上海:上海大学,2012.Liu P Y. Research on bionic hand-eye coordination control of service robot[D]. Shanghai:Shanghai University, 2012.
[12]张国亮.空间机器人遥操作中基于视觉的局部自主控制研究[D].哈尔滨:哈尔滨工业大学,2010.Zhang G L. Research on local autonomy based on vision in space robot teleoperation[D]. Harbin:Harbin Institute of Technology, 2010.
[13]李明富.生物视觉引导机构机制及机器人手眼协调研究[D].武汉:华中科技大学,2009.Li M F. Research on mechanisms inherent in biological vision guided motion and robotic hand-eye coordination[D]. Wuhan:Huazhong University of Science and Technology, 2009.
[14]Conti R, Fanelli F, Meli E, et al. A free floating manipulation strategy for autonomous underwater vehicles[J]. Robotics and Autonomous Systems, 2017, 87(1):133-146.
[15]Gao J, Proctor A, Alison P, et al. Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles[J]. Oceans, 2017, 142(7):666-675.
[16]李盛前.基于视觉技术的水下焊接机器人系统研究[D].广州:华南理工大学,2016.Li S Q. Study on system of underwater welding robot based on vision technology[D]. Guangzhou:Huanan University of Science and Technology, 2016.
[17]Lewis F L, Yesildirek A, Liu K. Multilayer neural-net robot controller with guaranteed tracking performance[J]. IEEE Transactions on Neural Networks, 1996, 7(2):388-399.
[2]封锡盛,李一平.海洋机器人30年[J].科学通报,2013,58(zII):2-7.Feng X S, Li Y P. Marine robot for 30 years[J]. Chinese Science Bulletin, 2013, 58(zII):2-7.
[3]Hordur J, Michael K, Brendan E, et al. Imaging sonar-aided navigation for autonomous underwater harbor surveillance[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2010:4936-4943.
[4]曾俊宝,李硕,李一平,等.便携式自主水下机器人控制系统研究与应用[J].机器人,2016,38(1):91-97.Zeng J B, Li S, Li Y P, et al. Research and application of the control system for a portable autonomous underwater vehicle[J].Robot, 2016, 38(1):91-97.
[5]Pere R, Marc C, David R, et al. Intervention AUVs:The next challenge[J]. Annual Reviews in Control, 2015, 40(1):227-241.
[6]刘泽发.观测型ROV航行控制系统设计及运动控制技术研究[D].杭州:浙江大学,2015.Liu Z F. Development of navigation control system and research on the motion control technique of observation-class ROV[D].Hangzhou:Zhejiang University, 2015.
[7]Oussama K, Yeh X, Gerald B, et al. Ocean One:A robotic avatar for oceanic discovery[J]. IEEE Robotics&Automation Magazine, 2016, 23(4):20-29.
[8]Cork P. Robotics, vision and control[M]. Berlin, Germany:Springer, 2014.
[9]Antonio C L, Fernando L. Passivity-based adaptive 3D visual servoing without depth and image velocity measurements for uncertain robot manipulators[J]. International Journal of Adaptive Control and Signal Processing, 2016, 30(8/9/10):1269-1297.
[10]Mohebbi A, Keshmiri M, Xie W. A comparative study of eye-in-hand image-based visual servoing:Stereo vs. mono[J]. Journal of Integrated Design and Process Science, 2015,19(3):25-54.
[11]刘鹏玉.服务机器人手眼协调仿生控制研究[D].上海:上海大学,2012.Liu P Y. Research on bionic hand-eye coordination control of service robot[D]. Shanghai:Shanghai University, 2012.
[12]张国亮.空间机器人遥操作中基于视觉的局部自主控制研究[D].哈尔滨:哈尔滨工业大学,2010.Zhang G L. Research on local autonomy based on vision in space robot teleoperation[D]. Harbin:Harbin Institute of Technology, 2010.
[13]李明富.生物视觉引导机构机制及机器人手眼协调研究[D].武汉:华中科技大学,2009.Li M F. Research on mechanisms inherent in biological vision guided motion and robotic hand-eye coordination[D]. Wuhan:Huazhong University of Science and Technology, 2009.
[14]Conti R, Fanelli F, Meli E, et al. A free floating manipulation strategy for autonomous underwater vehicles[J]. Robotics and Autonomous Systems, 2017, 87(1):133-146.
[15]Gao J, Proctor A, Alison P, et al. Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles[J]. Oceans, 2017, 142(7):666-675.
[16]李盛前.基于视觉技术的水下焊接机器人系统研究[D].广州:华南理工大学,2016.Li S Q. Study on system of underwater welding robot based on vision technology[D]. Guangzhou:Huanan University of Science and Technology, 2016.
[17]Lewis F L, Yesildirek A, Liu K. Multilayer neural-net robot controller with guaranteed tracking performance[J]. IEEE Transactions on Neural Networks, 1996, 7(2):388-399.