基于神经网络的多水下机器人协调控制方法研究
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摘要
21世纪是海洋的世纪。海洋对于人类的发展和社会进步将起到至关重要的作用。自主式水下机器人(AUV)系统是未来海洋探测和开发，以及完成各种水下智能作业任务的重要工具。作为水下机器人技术中的关键技术，研究如何实现水下机器人运动的精确控制以及多机器人系统控制具有重要意义。
本文首先研究了神经网络技术在水下机器人运动控制中的应用，为多机器人的协调控制提供基本技术保障。研究的重点在于通过优化神经网络算法以提高网络学习的收敛速度和提高网络学习的稳定性。实验证明，设计的神经网络控制器具有良好的控制效果，为水下机器人的运动控制开辟了一种新的思路和设计方案，这对研究自主式水下机器人的智能控制起到积极的推动作用。
其次我们对多水下机器人系统的分布式控制进行了探索性的研究，多水下机器人系统的群体组织方式与机器人混合控制结构，机器人之间的行为协调是本文的研究重点。试验的结果表明基于混合结构的行为协调可以构建一个有效的多机器人分布式控制系统，强化学习的引入使得系统可以得到较好的优化结果。绝大部分的工作都是建立在新的多水下机器人仿真器之上的，新的6自由度数学模型、限制水域及海流修正、声与非声传感器的模拟以及基于MAS的软件框架使得该仿真系统大大加速地逼近真实条件下的多机器人控制研究。
In twenty-one century, resources in oceans will play a very important role in the development of human society. Autonomous Underwater Vehicle (AUV) is a very useful tool in exploring and utilizing resources in oceans. As the key technology, the research on how to control and coordinate multiple AUVs has important significance in theory and practice.
Firstly, the application of neural network in the AUV's motion control is presented and discussed. The focus of research is on the optimization of neural network learning algorithm to increase the convergence velocity and the stability of learning. The experiment has verified that the neural network controller designed had good performance. The successful application of neural network controller in AUV's motion control has given a new way of controller design and will have positive effect in the research of intelligent control of AUV.
Then the exploration of the distributed control of the multi-AUV system is made, in which we focused on the structure of multi-AUV team, the hybrid AUV control architecture and the behavior coordination between AUVs. The experiments have proved that a valid team control system can be achieved based on the hybrid architecture and well-defined behaviors, and better results by introducing Reinforcement Learning theory. New 6 DOF simulation model, restrictive condition modification, acoustic and non-acoustic sensor simulation and the MAS based framework make the new multi-AUV simulator more close to the real arena for the controller, which is a great promotion to the development of
AUV team.
本文首先研究了神经网络技术在水下机器人运动控制中的应用，为多机器人的协调控制提供基本技术保障。研究的重点在于通过优化神经网络算法以提高网络学习的收敛速度和提高网络学习的稳定性。实验证明，设计的神经网络控制器具有良好的控制效果，为水下机器人的运动控制开辟了一种新的思路和设计方案，这对研究自主式水下机器人的智能控制起到积极的推动作用。
其次我们对多水下机器人系统的分布式控制进行了探索性的研究，多水下机器人系统的群体组织方式与机器人混合控制结构，机器人之间的行为协调是本文的研究重点。试验的结果表明基于混合结构的行为协调可以构建一个有效的多机器人分布式控制系统，强化学习的引入使得系统可以得到较好的优化结果。绝大部分的工作都是建立在新的多水下机器人仿真器之上的，新的6自由度数学模型、限制水域及海流修正、声与非声传感器的模拟以及基于MAS的软件框架使得该仿真系统大大加速地逼近真实条件下的多机器人控制研究。
In twenty-one century, resources in oceans will play a very important role in the development of human society. Autonomous Underwater Vehicle (AUV) is a very useful tool in exploring and utilizing resources in oceans. As the key technology, the research on how to control and coordinate multiple AUVs has important significance in theory and practice.
Firstly, the application of neural network in the AUV's motion control is presented and discussed. The focus of research is on the optimization of neural network learning algorithm to increase the convergence velocity and the stability of learning. The experiment has verified that the neural network controller designed had good performance. The successful application of neural network controller in AUV's motion control has given a new way of controller design and will have positive effect in the research of intelligent control of AUV.
Then the exploration of the distributed control of the multi-AUV system is made, in which we focused on the structure of multi-AUV team, the hybrid AUV control architecture and the behavior coordination between AUVs. The experiments have proved that a valid team control system can be achieved based on the hybrid architecture and well-defined behaviors, and better results by introducing Reinforcement Learning theory. New 6 DOF simulation model, restrictive condition modification, acoustic and non-acoustic sensor simulation and the MAS based framework make the new multi-AUV simulator more close to the real arena for the controller, which is a great promotion to the development of
AUV team.
引文
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[3] You Shang, Shuang Zhang, Xuemin Liu, Wanchun Zhang. Research on the planning strategies of multiple AUVs motion coordination. Proceedings of the 11th international symposium on unmanned untethered submersible technology. USA. 1999:P592-599
[4] A.J. Healey, F. Bahrke, J. Navarrete. Failure Diagnostics for Underwater Vehicles: A Neural Network Approach. Manoeuvring and Control of Marine Craft. 1990:P294-305
[5] Healey, A. "Application of Formation Control for Multiple Vehicle Robotic Minesweeping", Proceedings of the IEEE CDC-2001, Orlando, FL, December 2001
[6] Healey, A. J., Kim, Y., "Control And Random Searching With Multiple Robots", Proceedings IEEE CDC Conference 2000, Sydney Australia, Nov. 2000
[7] D. K. Atwood, J. J. Leonard, J. G. Bellingham and B. A. Moran. "An Acoustic Navigation System for Multiple Vehicles," Proceedings, International Symposium on Unmanned Untethered Submersible Technology, pages 202-208, New Hampshire, September, 1995.
[8] Bellingham, James G. New Oceanographic Uses of Autonomous
Underwater Vehicles. Marine Technology Society Journal, 31(3):34-47. Fall 1997.
[9] R.M. Turner and E.H. Turner. A two-level, protocol-based approach to controlling autonomous oceanographic sampling networks . In the IEEE Journal of Oceanic Engineering special issue on autonomous oceanographic sampling networks, vol. 26, no. 4, pp. 654-666, October, 2001
[10] Yoji Kuroda, Tamaki Ura: "Vehicle Control Architecture for Operating Multiple Vehicles", Proc. AUV94, July 1994, pp. 323-329
[11] Brumitt, B. L., Stentz, A., "Dynamic Mission Planning for Multiple Mobile Robots", Proceedings of the IEEE International Conference on Robotics and Automation, No. 3, pp. 2396-2401, 1996.
[12] Caloud, P., Choi, W., Latombe, J-C., Le Pape, C., and Yim, M., "Indoor Automation with Many Mobile Robots", IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 67-72, 1990.
[13] Chaimowicz, L., Sugar, T., Kumar, V., and Campos, M. F. M., "An Architecture for Tightly Coupled Multi-Robot Cooperation", IEEE International Conference on Robotics and Automation, 2001.
[14] Arkin, R. C., "Cooperation without Communication: Multiagent Schema-Based Robot Navigation", Journal of Robotic Systems, Vol. 9, No. 3, pp. 351-364, 1992.
[15] Brauer, W., and Wei., G., "Multi-Machine Scheduling - A Multi-Agent Learning Approach", Proceedings of the 3rd International Conference on Multi-Agent Systems, pp. 42-48, 1998. 10.
[16] Cicirello, V., and Smith, S., "Insect Societies and Manufacturing", The IJCAI-01 Workshop on Artificial Intelligence and Manufacturing: New AI Paradigms for
Manufacturing, 2001
[17] B(?)hringer, K., Brown, R., Donald, B., Jennings, J., and Rus, D., "Distributed Robotic Manipulation: Experiments in Minimalism", Proceedings of the International Symposium on Experimental Robotics (ISER), 1995
[18] Chevallier, D., and Payandeh, S., "On Kinematic Geometry of Multi-Agent Manipulating System Based on the Contact Force Information", The 6th International Conference on Intelligent Autonomous Systems (IAS-6), pp. 188-195, 2000.
[19] Desai, J. P., Ostrowski, J., and Kumar, V., "Controlling formations of multiple mobile robots", Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2864-2869, 1998.
[20] Decker, K. S., and Lesser, V. R., Designing i Family Of Coordination Algorithms, Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp. 73-80, 1995.
[21] Goldman, C. V., and Rosenschein, J. S., "Emergent Coordination through the Use of Cooperative State-Changing Rules", Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 408-413, 1994.
[22] Golfarelli, M., Maio, D., Rizzi, S., "A Task-Swap Negotiation Protocol Based on the Contract Net Paradigm", Technical Report CSITE, No. 005-97, 1997.
[23] Sandholm, T., "An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations", Proceedings, Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 256-262, 1993.
[24] Smith, R., "The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver", IEEE Transactions on Computers, Vol. C-29, No. 12, 1980.
[25] Stentz, A., and Dias, M. B., "A Free Market Architecture for Coordinating Multiple Robots", Technical report, CMU-RI-TR-99-42, Robotics Institute, Carnegie Mellon University, 1999.
[26] Laengle, r., Lueth, T. C., Rembold, U., and Woern, H., "A distributed control architecture for autonomous mobile robots-implementation of the Karlsruhe Multi-Agent Robot Architecture (KAMARA)", Advanced Robotics, Volume 12, No. 4, pp. 411-431, 1998.
[27] Simmons, R., Singh, S., Hershberger, D., Ramos, J., and Smith, T., "First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly", Proceedings of the International Symposium on Experimental Robotics (ISER), 2000.
[28] Dias, M. B., Stentz, A., "A Free Market Architecture for Distributed Control of a Multirobot System", The 6th International Conference on Intelligent Autonomous Systems (IAS-6), pp. 115-122, 2000.
[29] 戴学丰，边信黔．6自由度水下机器人轨迹控制仿真研究。系统仿真学报．2001，13(3)：368-370
[30] 施生达．潜艇操纵性．国防工业出版社．1995：159-161
[31] Sarkar, Nilanjan Podder, Tarun Kanti. Motion coordination of underwater vehicle-manipulator systems subject to drag. Proceedings - IEEE International Conference on Robotics and Automation. 1999,1:387-392
[32] Cristi, Roberto Caccia, Massimo Veruggio, Giamma. Motion estimation and modeling of the environment for underwater vehicles International Journal of Systems Science. 1998,29(10).:1135-1143
[33] McMillan, Scott Orin, David E. McGhee, Robert B. Computational framework for simulation of underwater robotic vehicle
systems. Autonomous Robots. 1996,3(2-3).:253-268
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