Abstract
This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formations. A hybrid trajectory planning algorithm based on potential field fluid dynamic model and bidirectional fast search random tree is proposed to improve the ability of UAV formation to adapt to complex dynamic environment. Firstly, a dynamic system mathematical model based on fluid potential energy field is proposed; and the obstacle potential energy function and potential energy function between the formations modify the disturbance flow field. Secondly, IBi-directional Rapidly Exploring Random Tree (IBi-RRT) algorithm with adaptive step size is scheduled to solve the dispersive and convergent streamlines of disturbed flow field and to plan the trajectory. This method can clarify the flow field streamlines by adaptive step size combined with rolling detection method, which greatly improves the formation's ability to avoid dynamic threats. The experimental results show that the proposed improved fluid potential energy field dynamic system and IBi-RRT hybrid trajectory planning algorithm with adaptive step size can effectively improve the adaptive ability of UAV formation to the dynamic environment, and can plan the ideal trajectory in response to unexpected situations.
Highlights
In recent years, UAV have been widely used in military and civilian fields to perform tasks such as intelligence, search, surveillance, border patrol, threat reconnaissance and elimination, target tracking, target rescue
Inspired by the idea of Yao et al [11], this paper proposes an hybrid trajectory planning algorithm based on potential field fluid dynamic model and IBi-directional Rapidly Exploring Random Tree (IBi-RRT) algorithm that introduces an adaptive step-size rolling detection method, which can detect the situation of sudden threat and plan an ideal trajectory in complex environment real time for UAV formation
EXPERIMENTAL RESULTS AND ANALYSIS In order to verify the effectiveness of trajectory planning algorithm based on potential field fluid dynamic model and IBi-RRT with adaptive step size proposed in this paper
Summary
UAV have been widely used in military and civilian fields to perform tasks such as intelligence, search, surveillance, border patrol, threat reconnaissance and elimination, target tracking, target rescue. While UAV formation can efficiently achieve more complicated and difficult tasks, there are many uncontrollable factors in the complex dynamic environment, such as dynamic obstacles, sudden threat, radar irradiation, tasks changed and so on. These circumstances greatly affect the viability and operational efficiency of UAV formation. UAV formation needs to assist, supply each other and separate through obstacle areas, so that UAV formation can adapt to the changes of complex mission environment [1] and complete the reconfiguration of formation in real-time. How to improve the autonomy of UAVs becomes the focus of researchers. Obstacle avoidance and reconfiguration of UAV formation [2], [3] is an important research
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