Research on UAV route planning based on adaptive polymorphic ant colony algorithm

Abstract

Aiming at the problem of traditional UAV trajectory planning which falls into local optimum easily, and the problem of UAV trajectory planning in complex terrain, a trajectory planning method based on adaptive polymorphic fusion ant colony algorithm is proposed. This study describes the problem of route planning, establishes a mathematical model, and combines the adaptive ant colony algorithm with the polymorphic ant colony algorithm to form a global and local parallel search mode, which improves the ability of the algorithm to find the global optimal value. An adaptive parallel strategy and an adaptive information update strategy are proposed to improve the global search ability. Simulation results show that this method has better performance than the other two traditional ant colony algorithm and polymorphic ant colony algorithms. It can effectively improve the length and convergence speed of the search path and avoid falling into local optimum in the solution process. Therefore, the adaptive polymorphic fusion ant colony algorithm has a good application prospect in solving the optimal track planning problem.

Keywords

• computer simulation
• uav
• track planning
• polymorphic ant colony
• adaptive parallel strategy