Anytime recovery sparse A<*> and Kalman filtering-based dynamic route planning method

Abstract

The invention discloses an anytime recovery sparse A<*> and Kalman filtering-based dynamic route planning method, and belongs to the technical field of unmanned aerial vehicle route planning. The implementation method comprises the steps of firstly, estimating dynamic threat and a position of a moving target in a next moment task environment by a self-adaptive expansion Kalman filtering algorithmwithin a target observation period T according to a rolling planning strategy, and taking an estimation result as input information of unmanned aerial vehicle route planning; and secondly, performingunmanned aerial vehicle rapid route planning by employing the Anytime recovery sparse A<*> algorithm so that an unmanned aerial vehicle can sense the environmental changes in advance, a route direction is changed, a practical route is output within limited time, the dynamic threat is smoothly prevented and a target region is accurately reached. Under the condition that the dynamic threat and the moving target exists in a real task environment of the unmanned aerial vehicle, all threats all can be enabled to be prevented by the unmanned aerial vehicle, and the time of reaching the target regionis shortened. The anytime recovery sparse A<*> and Kalman filtering-based dynamic route planning method has the characteristics of high solution efficiency and high robustness.

Description

technical field [0001] The invention relates to a dynamic track planning method based on repaired Anytime sparse A* and Kalman filtering, and belongs to the technical field of unmanned aerial vehicle track planning. Background technique [0002] There are many unknown and uncertain factors in the real combat environment of drones. For example, the uncertainty of unknown threat bursts, the uncertainty of dynamic threats and target motion parameters, etc., the interaction and mutual restriction between various uncertain factors make the real mission environment situation of UAVs change with time. It is highly dynamic. Therefore, it is necessary for the UAV to estimate the motion parameters of the dynamic factors in the mission environment. Through pre-estimation, the UAV can perceive changes in the dynamic environment in advance, adjust the flight path in time, avoid dynamic threats in the mission environment, and shorten the time to reach the target area. time and improve t...

AI Technical Summary

Problems solved by technology

However, the traditional trajectory planning algorithm is difficult to meet the timeliness and reliability requirements of the UAV in a dynamic environment. The Anytime Restorative Sparse A* (AR-SAS) algorithm is adopted to combine the Anytime algorithm architecture with the sparse A* algorithm. Can effectively solve the above-mentioned trajectory planning problems with time constraints

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