Air route planning method based on guide speed field in threat environment

Abstract

The invention discloses an air route planning method based on guide speed field in threat environment, and belongs to the field of air route planning. Subsistent threat affecting is fully considered, the threat is equivalent to a circular obstacle in a two-dimensional plane through the calculation, then traction field and avoidance field environment is established, the guide speed field is specially designed, controllability of the algorithm is improved, and the fast advantage of artificial potential field method is maintained. According to the method, planning speed can be directly obtained by calculation, the problem of incapability of reaching or entry barriers of the artificial potential field method can be overcome, and a smooth two-dimensional obstacle avoidance air route can be planned in real time. The air route planning method has the advantages of simple terrain modeling and small amount of calculation, and is easy to realize.

Description

technical field [0001] The invention belongs to the field of route planning, and specifically refers to a route planning method based on a guidance velocity field in a threat environment. Background technique [0002] As a research hotspot in recent years, intelligent agents have attracted more and more attention from various countries in the world. The most obvious sign of an agent is autonomy, and an important branch of the autonomous research field is route planning. For a long time, route planning has relied on manual work by ground personnel. This method is not only time-consuming and labor-intensive, but often fails to obtain the best route. No matter on the ground, underwater or in the sky, due to the diversification of roles played by the agent, the environment in which it performs tasks is becoming more and more complex, such as the strengthening of ground air defense firepower and the enhancement of the radar system's ability to capture targets, leaving the agent'...

AI Technical Summary

Problems solved by technology

For example, the A* algorithm has strong convergence and rapidity, but it must rely on grid division. The density of grid division affects the accuracy of the algorithm, and the route is not smooth enough; while the genetic algorithm and ant colony algorithm with random characteristics It has good globality and can often find the optimal solution, but its convergence speed is slow, which is not conducive to engineering applications; the artificial potential field method has a simple algorithm, fast calculation speed and easy implementation, and has good real-time performance, but it cannot Deals with threat effects and has only repulsive and attractive fields, which can lead to route planning failures when the potential fields are balanced

[0004] As the tasks performed by the agent become more and more complex, and there are more and more dangerous areas, the traditional artificial routes cannot meet the development requirements of the agent's autonomous planning.

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