Heuristic and random search algorithm in optimization of route planning for Robot’s geomagnetic navigation

Abstract

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.