基于改进势场蚁群算法的波浪动力滑翔器路径规划算法研究

【作者】 赵红；

【导师】 高军伟；

【作者基本信息】 青岛大学 ， 控制科学与工程， 2016， 硕士

【摘要】 波浪动力滑翔器作为一种以波浪能为驱动力的新型海洋观测平台,在海洋环境监测技术领域具有划时代的意义。波浪动力滑翔器依靠其独特的双体结构将波浪能转化为前行推动力,弥补了传统海洋监测工具需要定期能源补给的缺点,不仅节约了能源、减少了花费,而且具有高强的续航能力和环境适应性。然而复杂多变的海洋环境和波浪动力滑翔器的动力源特点,使波浪动力滑翔器的路径规划变得尤为困难,传统的路径规划算法已经不能满足波浪动力滑翔器的航行需求。因此为了规划出一条航行速度快、花费时间短、无碰撞的最优航行路线,需要设计一种适合于波浪动力滑翔器特点的新型算法。传统的路径规划算法有很多,如模拟退火算法、遗传算法、禁忌搜索算法、蚁群算法等等。然而,与其他路径规划算法相比,蚁群算法具有适合规划波浪动力滑翔器路径的一大优势,即能够利用信息正反馈机制动态地响应外界环境的变化并能够通过分布式并行计算机制提高运算效率。因此选用蚁群算法作为规划波浪动力滑翔器路径的基础算法。由于波浪动力滑翔器航行速度完全依赖于周围的环境,因此需要对传统蚁群算法进行改进。首先将人工势场合力引入到启发信息中组成势场蚁群算法来弥补蚁群算法存在的不足,其次综合考虑影响波浪动力滑翔器速度的主要环境因素,之后采用精英策略改进迭代过程中的信息素更新策略,最后根据障碍物漂移情况,实时改变波浪动力滑翔器的路径。将改进后的势场蚁群算法作为波浪动力滑翔器的路径规划算法。利用栅格算法搭建不同的海洋环境模型,验证改进后的势场蚁群算法的性能。从仿真结果可以看出,改进的势场蚁群算法能适用于不同的海洋环境,可根据波浪动力滑翔器的特点寻找路径短、速度快、无碰撞的最优路线,从而证明了该混合算法的实用性和有效性。更多还原

【Abstract】 The wave glider(WG) driven by wave energy is a new marine environmental monitoring platform, which has an epoch-making significance in the field of international marine environmental technology today. The wave glider depends on its unique double-body structure to convert wave energy into the forces propelling it forward, which makes up the shortfall that the traditional marine monitoring tools need refueling on a regular basis. It not only saves energy, reduces cost, but also has high cruising ability and environment adaptability.However, the complexity of marine environment and the power source characteristics of wave glider make the path planning of wave glider particularly difficult.And the traditional path planning algorithm cannot meet the navigating demand of wave glider. Therefore in order to plan out the optimal navigation path, where the speed of wave glider is fast, the time spent is short and there is no collision with obstacles, it is necessary to design a kind of new algorithm in view of the characteristics of wave glider.There are a lot of the traditional path planning algorithms, such as simulated annealing algorithm, genetic algorithm, tabu search algorithm, ant colony optimization algorithm, and so on. However, compared with other path planning algorithms, the ant colony optimization algorithm(ACO) has a big advantage, which makes the ACO suitable for planning the path of wave glider. The big advantage is that the ant colony optimization algorithm can dynamically respond to the changes of external environment by the positive feedback mechanism and improve the computational efficiency by the parallel computation mechanism. So select the ant colony optimization algorithm as the basis algorithm for planning the path of wave glider.Because the speed of wave glider is entirely dependent on the surrounding environment, the traditional ant colony optimization algorithm need be improved. Firstly,the algorithm introduces artificial potential field(APF) force into heuristic information to ant colony optimization with potential field(ACOPF) for overcoming the disadvantages of ant colony optimization algorithm. Secondly, take into consideration the main environmental factors which affect WG speed. Thirdly, improve the update strategy of pheromone during iteration by means of elitist strategy. Finally, change the WG path in real time according to the drift of dynamic obstacles. The improved ant colony optimization with potential field will become the path planning algorithm of wave glider.In order to verify the performance of the improved ant colony optimization with potential field, set up some different marine environment models with grid algorithm. The simulation results show that the improved ant colony optimization with potential field not only applies for different marine environments, but also searches the optimal path by synthesizing distance, time and obstacles avoidance according to the characteristics ofwave glider. The experiment validates the practicability and validity of the hybrid algorithm更多还原