To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.

为了提高效率和准确性，提出了一种新的路径规划组合算法，其中考虑了水下地磁匹配导航区域和环境约束的分布。首先，利用地磁导航匹配区域，Dijkstra算法可以获得主要路径点。其次，建立并规范化了环境约束模型，建立了路径规划环境约束成本模型。第三，结合环境约束成本模型中时间，函数关系，约束条件和变量之间的关系，提出了粒子群算法。利用主要路线品脱路线，路线规划将转化为路线优化。最后，将主要路径点作为粒子群优化算法的初始输入，改进了粒子群惯性权重的选择方法和粒子编码方法。实现了Dijkstra算法的最优路径规划和粒子群优化。仿真结果表明，搜索空间的粒度可以得到最小的评估，搜索范围更窄，搜索效率更高。组合算法既保证全局最优，又保证局部最优，可以有效避免不匹配的导航区域，实现高效的路径规划功能。实现了Dijkstra算法的最优路径规划和粒子群优化。仿真结果表明，搜索空间的粒度可以得到最小的评估，搜索范围更窄，搜索效率更高。组合算法既保证全局最优，又保证局部最优，可以有效避免不匹配的导航区域，实现高效的路径规划功能。实现了Dijkstra算法的最优路径规划和粒子群优化。仿真结果表明，搜索空间的粒度可以得到最小的评估，搜索范围更窄，搜索效率更高。组合算法既保证全局最优，又保证局部最优，可以有效避免不匹配的导航区域，实现高效的路径规划功能。