基于改进粒子群算法的水下滑翔机路径规划研究

发布时间：2018-07-03 15:05

本文选题：水下滑翔机 + 路径规划　；参考：《吉林大学》2017年硕士论文

【摘要】：水下滑翔机具有无动力推动的特点,可以把温差能转换为机械能。依靠自身携带的能源调整姿态和俯仰角度,利用水下温差能实现水下滑翔。克服了传统的水下机器人的能耗限制,具有更大范围,更长时间进行海下探测的能力;同时由于无动力推进,噪声极小,隐蔽性强,具有很高的实用价值。路径规划是水下滑翔机的重要研究课题,路径规划问题是值如何在确定的环境中,按照某个优化目标取得最优的路径。建立水下滑翔机的能耗模型首先要理解其工作原理,本文从水下滑翔机的受力分析出发,剖析其系统组成,详细分析了从温差能转化为机械能的过程。水下滑翔机的动力来源于海洋温差能,根据其动力系统对温度敏感的特点,本文使用美国国家海洋学数据中心海洋气候实验室的数据产品WOA13北纬10°到20°,东经110°到120°之间海域2013年1月份的温度数据,对能达到温度要求的海域进行定量区分,得出区分特定水下滑翔机适航与否的地形图。同时对相隔0.25纬度、经度的测量点数据进行了细化,得到了相邻的数据点相差0.05纬度、经度的适航区域地形图。准确的环境数据是决定路径规划效率的重要前提。本文使用改进的粒子群算法对水下滑翔机的路径进行规划,针对粒子群算法初始阶段随机生成粒子的不稳定性影响算法性能问题,本文利用反学习方法初始化粒子群,选择适应度最好的半数粒子作为初始种群,加快了算法的收敛速度。因为路径导航受到路径长度和水深两部分因素的影响,本文引入惩罚函数优化适应度函数的计算。为了进一步强化PSO算法的深度搜索能力,避免陷入局部最优,本文结合差分进化操作,发掘当前种群周围的优秀解。仿真实验表明,本文改进的粒子群算法具有更快的收敛速度、同时得到更优的规划路线。
[Abstract]:The underwater glider has the characteristic of no driving force, and can convert the temperature difference energy into mechanical energy. The underwater gliding can be realized by adjusting the attitude and pitch angle of the energy carried by itself and utilizing the underwater temperature difference. It overcomes the limitation of energy consumption of the traditional underwater vehicle, and has the ability to carry out subsea detection in a larger range and longer time. At the same time, because of the powerless propulsion, the noise is very small, the concealment is strong, so it has high practical value. Path planning is an important research topic for underwater gliders. The path planning problem is how to obtain the optimal path according to an optimal target in a given environment. In order to establish the energy consumption model of underwater glider, it is necessary to understand its working principle. Based on the stress analysis of underwater glider, this paper analyzes its system composition and the process of converting temperature difference energy into mechanical energy in detail. The underwater glider's power comes from the ocean temperature difference energy, according to its power system is sensitive to the temperature characteristic, In this paper, the temperature data of January 2013 in the sea area between latitude 10 掳to 20 掳N and longitude 110 掳to 120 掳E from WOA13 N to 20 掳and longitude 110 掳to 120 掳East are used to quantitatively distinguish the sea areas that can meet the temperature requirements, using the data from the Marine Climate Laboratory of the National Oceanographic data Center of the United States of America. A topographic map is obtained to distinguish the seaworthiness of a particular underwater glider. At the same time, the data of measuring points separated by 0.25 latitudes and longitude are refined, and the difference of adjacent data points is 0.05 latitude and longitude, and the navigable area topographic map is obtained. Accurate environmental data is an important prerequisite to determine the efficiency of path planning. In this paper, improved particle swarm optimization (PSO) algorithm is used to plan the path of underwater glider. In view of the effect of random particle instability in the initial stage of PSO on the performance of the algorithm, the inverse learning method is used to initialize the PSO. The convergence rate of the algorithm is accelerated by selecting the half particle with the best fitness as the initial population. Because the path navigation is affected by the path length and water depth, the penalty function is introduced to optimize the fitness function. In order to further enhance the depth searching ability of PSO algorithm and avoid falling into local optimum, this paper combines differential evolution operation to discover the excellent solutions around the current population. The simulation results show that the improved particle swarm optimization algorithm has faster convergence speed and better planning path.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U674.941;U664.82;TP18

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