【作者】 王岚；

【导师】 孟庆鑫；

【作者基本信息】 哈尔滨工程大学 ， 控制理论与控制工程， 2003， 博士

【摘要】 拱泥机器人是一种在水下完成打洞穿缆特种作业机器人。沉船打捞作业中的一项主要工序是水下攻打千斤洞，即在沉船底部清除出一条穿引抬船钢缆的通道，目前攻打千斤洞一般采用手动攻泥器，由潜水员手工操作，劳动强度大，效率低，且危险性高。拱泥机器人能够在水下泥土环境中按规划轨迹完成攻打千斤洞作业任务，以替代潜水员的手工操作，实现自动化的水下攻打千斤洞作业。拱泥机器人的控制与路径规划是拱泥机器人研究中的一项关键技术，为拱泥机器人按规划轨迹完成攻打千斤洞作业提供了技术保障。本文在国家自然科学基金项目的资助下，对拱泥机器人的控制系统及其路径规划问题进行了深入研究。 文中概述了与本课题相关的国内外研究与发展现状。综述了移动机器人智能控制理论的发展、智能控制的方法和国内外研究状况，评述了移动机器人路径规划的基本方法、研究和国内外发展现状。 对拱泥机器人运动学和动力学进行了研究。本文针对拱泥机器人蠕动爬行的特点，建立了拱泥机器人的三维运动学模型，并进行了仿真验证。分析了拱泥机器人在水下泥土环境中受力情况，在此基础上采用Routh方程建立了拱泥机器人蠕动爬行机构分解子运动的动力学方程。 根据拱泥机器人工作环境具有很强不确定性的特点，本文将传统PID控制与神经网络相结合，设计了一种基于BP神经网络的自适应PID控制器，该控制器通过BP神经网络在线自动整定PID参数，使拱泥机器人具有一定的自适应能力。通过对拱泥机器人头部的受力分析，建立了头部转动自由度的神经网络模型，用来提供BP神经网络学习时所需要的梯度信息。 研究了拱泥机器人的路径规划方法。设计了基于模糊神经网络的实时局部路径规划器。拱泥机器人的路径规划策略是首先进行离线全局规划，然后，在拱泥机器人行走时，实时局部路径规划器在全局规划的基础上，根据传感器获得的实时环境信息进行在线规划，实时调整规划的路径，确定当前的局部子目标，使拱泥机器人最终完成攻打千斤洞的任务。 设计并实现了拱泥机器人样机的控制系统。根据拱泥机器人的机械结构特点和特定的作业环境，采用上、下位机两级控制的方案，上位机为PC机，哈尔滨工程大学博士学位论文负责拱泥机器人规划工作，下位机为AT9054433单片机，上下位机之间采用串口进行通讯。建立了拱泥机器人的室内实验平台，进行了拱泥机器人原理样机的实验研究，获取了必要的试验数据，为进一步研制工程化样机奠定了坚实的基础。更多还原

【Abstract】 Move-in-mud robot is a new-type and special-use underwater robot. Now, the main method to salvage the wrecked vessel is to using buoy. Punching a hole in the mud under the sea is the key technology of salvaging the wrecked vessel using buoys. At present, divers finish the work of punching a hole manually. The work is laborious, inefficient and dangerous. The move-in-mud robot can perform the hole excavating work to benefit sling passing in the planned trajectory in the mud under water. The control system and path planning are the essential technology in the move-in-mud robot’ s research, which is the premise and ensurence for the robot to accomplish the hole excavating work through planned trajectory. With the support of "Basic Technology Research on move-in-mud Robot Based on Worm Principle" by the National Natural Science Funds, research on move-in-mud robot’ s control system and path planning are developed in this paper.The development and present situation of the mobile robot, its method of path planning are summarized in the paper. The development of the control theory, the method of intelligent control and present study situation in mobile robot domain are also introduced in the paper.Kinematics and dynamics of the move-in-mud robot have been studied. Aimed at the vermiculation characteristics, kinematics model of the move-in-mud robot is established. Then the forces on the move-in-mud robot under the ocean’ s mud are analyzed. At last, the dynamics equations of the move-in-mud robot are established.Because of the strong uncertainty of the move-in-mud robot’ s working environment, a kind of adaptive PID control method using BP neural network is proposed. The BP neural network is used to modify the parameter and makes the move-in-mud robot have adaptive capacity.The neural networks model of the head movements of the move-in-mud robot is established by investigating the head movement model of the move-in-mud robot and it is designed to provide grads data for the BP neural network learning.The path planning method for the move-in-mud robot is investigated in this paper. A kind of local path planner based on fuzzy neural network is proposed in this paper. The path planning strategy is that: firstly, layout the off-line global path; then, when the robot is moving, the local path planner gets the sensors’ data, makes on-line planning, modifies the real-time path and decides the sub-goal.The control system of the move-in-mud robot prototype is designed in this paper. Based on the mechanical characteristic and special working environment of the move-in-mud robot, two level computer control scheme is adopted. The PC computer is chosen as the host, and the AT90S4433 single-chip is chosen as the slave. The serial communication is used for exchanging data between the host and the slave. With the experiment platform established in laboratory, the experiment researches on the move-in-mud robot prototype are made. The experiment results show that the control scheme proposed is feasible. The prototype makes stability foundation for engineering sample of next step.更多还原