Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

【摘要】：针对无人机编队在3维障碍环境中避障及位置速度一致性问题,提出一种基于改进人工势场(IAPF)和一致性理论的协同编队分布式避障控制算法。 . 首先，建立了无人机的粒子模型和二阶系统的动力学模型，利用图论知识描述了系统通信网络的拓扑结构。其次，定义了包含协调增益因子的吸引力势场函数、包含排斥力和规划角影响因素的排斥势场函数以及包含通信权重的无人机之间的势场函数。然后，通过编队中心的参考向量和预期速度分别改进共识协议中的位置和速度变量，结合IAPF和共识理论设计了一种新的编队避障控制协议。最后利用Lyapunov函数证明算法的稳定收敛性。仿真结果表明，该方法不仅可以在避开静态和动态障碍物的同时防止无人机相互碰撞，而且可以使无人机快速恢复预期编队，实现相对距离、相对高度和速度的一致性。并结合IAPF和共识理论设计了一种新的编队避障控制协议。最后利用Lyapunov函数证明算法的稳定收敛性。仿真结果表明，该方法不仅可以在避开静态和动态障碍物的同时防止无人机相互碰撞，而且可以使无人机快速恢复预期编队，实现相对距离、相对高度和速度的一致性。并结合IAPF和共识理论设计了一种新的编队避障控制协议。最后利用Lyapunov函数证明算法的稳定收敛性。仿真结果表明，该方法不仅可以在避开静态和动态障碍物的同时防止无人机相互碰撞，而且可以使无人机快速恢复预期编队，实现相对距离、相对高度和速度的一致性。