摘要：自治水下机器人（AutomaticUnderwaterVehicle，简称AUV）是海洋探测和开发的重要工具。随着科学技术的不断提高，海洋搜寻的不断推进以及实际应用要求的不断提升，对水下机器人搜寻算法的研究提出了更加高的要求。因为水下机器人工作环境复杂多变，条件恶劣，所以需要设计一个可靠的搜寻算法来保证工作高效顺利的进行。本论文基于生物启发神经网络模型来研究多AUV搜寻控制算法，将多AUV系统里的离散的栅格地图抽象为生物启发模型里的神经网络神经元，未搜寻区域吸引多AUV运动可以看成神经网络模型里神经元的外部激励信号，障碍物排斥多AUV运动可以看成神经网络模型里神经元的外部输入的抑制信号，神经元活性值随信号发生变化，根据神经网络中神经元的活性输出值分布情况自主规划AUV的搜寻路径。同时，提出了加入分区的生物启发多AUV合作搜寻，并且对多AUV在无故障情况下和有故障情况下进行了仿真研究。最后，对各种情况下的搜寻性能进行统计和分析。91337

毕业论文关键词：自治水下机器人，多AUV系统，生物启发模型，分区搜寻

Abstract： Autonomous Underwater Vehicle (AUV) is an important tool for ocean exploration and development in China。 With the continuous advancement of science and technology and ocean search and the continuous application of practical requirements, the search algorithm of its research put forward higher requirements。 Due to the poor working conditions of underwater robots and the complexity of the surrounding environment, it is essential to design a reliable search algorithm to ensure that the work is critical。 In this paper, the multi-AUV search control algorithm is studied based on the biological-inspired neural network model。 The discrete raster

maps in the multi-AUV system are abstracted into the neural network neurons in the biological-inspired model。 The non-search area attracts multiple AUVs In the network model, the external excitation signal and the obstacle rejection of the neuron can be regarded as the suppression signal of the external input of the neuron in the neural network model。 The neuronal activity value changes with the signal。 According to the activity output of the neuron in  the neural network Value distribution of the autonomous planning AUV search path。 At the same time, the bio-inspired multi-AUV cooperative search is proposed, and the multi-AUV  is simulated in the case of no fault and faulty。 Finally, the search performance in each case is analyzed and analyzed。

Key words: Autonomous underwater vehicle, multi - AUV system, biological - inspired model, partition search

目录

1绪论 4

1。1研究背景和意义 4

1。2水下机器人 4

1。2。1水下机器人分类 5

1。2。2水下机器人发展趋势 5

1。3搜寻算法研究现状 6

1。4论文主要内容 9

2基于生物启发模型的多AUV目标搜索算法 9

2。1生物启发原理 9

2。2基于生物启发二维神经网络模型 10

2。3模型稳定性分析 11

2。3。1二维神经网络稳定性分析 11

2。4小结 12

3二维环境中多AUV协作搜寻研究 12

3。1引言 12