Attack-defense countermeasure considered intelligent planning method for unmanned aerial vehicle track

Abstract

The invention relates to an attack-defense countermeasure considered intelligent planning method for an unmanned aerial vehicle track. According to the method, on an occasion that an attack-defense countermeasure exists in a task execution process of an unmanned aerial vehicle, an optimal objective function of a survival probability is converted from a product maximization form into a minimizationform, and a topographic condition and a constraint condition for track planning are added into a search algorithm, so that a planned track better conforms to an actual situation, and furthermore, a search space is reduced, and the track planning efficiency is improved. Compared with the prior art, the attack-defense countermeasure considered intelligent planning method disclosed by the inventionhas the advantages of capability of realizing efficient and rapid path planning for the unmanned aerial vehicle under the conditions of an air defense threat and a performance limit.

Description

technical field [0001] The present invention relates to a UAV trajectory planning method, in particular to an UAV trajectory intelligent planning method considering offensive and defensive confrontation. Background technique [0002] UAVs can break through the enemy's defense zone at ultra-low altitude and attack important targets located in the depth of the enemy. It has the characteristics of strong penetration capability, high hit accuracy and low cost. It is a new type of subversive weapon. [0003] A key basic technology for UAVs to exert combat effectiveness is trajectory planning. The purpose of trajectory planning is to plan a trajectory with a high probability of survival, a suitable voyage length, and satisfying its own performance constraints, under the premise of comprehensively considering factors such as flight time, fuel consumption, threat, and flight area, and using information such as terrain and battlefield environment. [0004] Many related trajectory pl...

AI Technical Summary

Problems solved by technology

[0005] In 2003, Nikolos et al. used B-spline curves to simulate the three-dimensional flight path of unmanned aerial vehicles, and used evolutionary algorithms to optimize the control points of B-spline curves. The limitation of this algorithm is that it cannot handle the constraints of the trajectory

[0007] 1) The trajectory planning of unmanned aerial vehicles is a coupling process of multi-objective optimization. It needs to consider various factors such as aircraft performance, threat sources, and formation coordination. The evaluation index has high information dimension and poor robustness. The search strategy loses stability, so it is difficult to find a pertinent, objective and multi-faceted evaluation index

[0008] 2) Conventional search algorithms tend to fall into local optimal solutions when used in path planning, such as methods based on gradient calculations and genetic algorithms. Will cause failure of path planning, instability and waste of voyage

[0009] 3) Some conventional search algorithms have high computational complexity, low computational efficiency, and slow convergence speed. The improvement of global optimization performance is often at the expense of the speed of the time dimension, and an ideal planning path cannot be obtained after a short iterative calculation

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