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Cooperative Formation and Obstacle Avoidance Algorithm for Multi-UAV System in 3D Environment

机译:3D环境下多无人机系统协同编队与避障算法

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Multi-UAV cooperative formation has become a hot research issue. Up to now, obstacles and other UAVs are still the main factors that threaten the flight safety. In this paper, we present a solution for formation flight and obstacle avoidance of unmanned aerial vehicles (UAVs) which can achieve cluster situational awareness, autonomous formation control and intelligent collaborative decision making. The main idea of this algorithm is to consider all UAV members as a whole and control the internal and external of the UAV system separately. Inside the system, a communication topology is set up. They use consensus algorithm to keep formation and avoid collision between UAVs. Moreover, we use the weight coefficient to set the priority for every UAV. Outside the system, an improved artificial potential field method is built for UAV system. We improved the safety distance and the traditional artificial potential to make them more suitable for the UAV obstacle avoidance task. Even when UAVs reach obstacles at high speed and small angle, they will have enough time and space to change flight direction. The internal control algorithms and external control algorithms are integrated to form the virtual total force by modifying the Reynolds clustering rules. Three track instructions are obtained from this virtual force. Finally, the effectiveness of cooperative obstacle avoidance algorithm is proved by MATLAB.
机译:多无人机合作组建已成为研究的热点。到目前为止,障碍物和其他无人机仍是威胁飞行安全的主要因素。在本文中,我们提出了一种无人飞行器(UAV)的编队飞行和避障解决方案,可以实现集群态势感知,自主编队控制和智能协作决策。该算法的主要思想是将所有UAV成员作为一个整体考虑,并分别控制UAV系统的内部和外部。在系统内部,建立了通信拓扑。他们使用共识算法来保持编队并避免无人机之间的冲突。此外,我们使用权重系数为每个无人机设置优先级。在系统外部,为无人机系统建立了一种改进的人工势场方法。我们提高了安全距离和传统的人工潜能,使其更适合于无人机避障任务。即使无人机以高速度和小角度到达障碍物,它们也将有足够的时间和空间来改变飞行方向。通过修改雷诺兹聚类规则,内部控制算法和外部控制算法被集成以形成虚拟总力。从该虚拟力获得了三个跟踪指令。最后,通过MATLAB证明了协同避障算法的有效性。

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