Carful planning of the flight is equally important for both manned and unmanned aerial operations. The paper addresses the problem of local path planning and static obstacle avoidance for an autonomous HALE-class fixed-wing UAV. Presented idea combines Dubins airplane model with Rapidly-exploring Random Tree algorithms to find a kinematically admissible and obstacle-free path through a 3D obstacle map. The algorithm is developed to be finally deployed on an embedded platform, thus it favors simplicity and computation performance, while maintaining probabilistic optimality. The paper begins with a short introduction and the research background. Then, used algorithms are described and discussed, followed by their verification in simulation environment using airplane guidance models. Conclusions are future remarks complete the paper.
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