New Local Path Replanning Algorithm for Unmanned Combat Air Vehicle

A path planning scheme for unmanned combat air vehicle (UCAV) is developed for achieving optimal local path replanning under complicated air-battle environment. Constructing and searching an improved Voronoi diagram based on the locations and grades of the different threats, Dijkstra algorithm is implemented to find an initial threat-avoiding flight path to the target. For matching dynamic battlefield situations and tracking the changing status of suddenly appeared threats, switching linear dynamic system (SLDS) model based on mix-state dynamic Bayesian network (mix-state DBN) is exploited. Viterbi approximation algorithm is then used to estimate the location and the grade of the suddenly appeared threat. Based on the detected states of new threat, Dijkstra algorithm is used again to find the replanned path and further optimized by performing cubic spline and sequential quadratic processing. The Matlab simulation result demonstrates the path planning algorithm is effective

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