论文标题
在密集的环境中，避免使用主动传感系统的弹性无人机群形成
Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment
论文作者
论文摘要
本文提出了一个感知共享和群轨迹全球最佳（stgo）算法融合无人机形成运动计划框架在主动传感系统的帮助下。首先，每个无人机收到的点云都由高斯混合模型（GMM）拟合并在群中传播。从收到的GMM中重新采样有助于全球地图，该地图用作共识的基础。其次，为了提高飞行安全性，主动传感系统旨在计划每个UAV的观察角，考虑到未知场，视野的重叠（FOV），速度方向和偏航旋转的平滑度以及该计划问题由分布式粒子群群优化（DPSO）algorithm解决。最后，为了确保避免障碍物的形成运动计划，允许形成结构进行仿射转化，并将其视为B型链条控制点的软限制。此外，引入了施工以避免局部最小值。 GMM沟通和StapGO的结合确保了无人机之间的安全和严格的共识。模拟中不同编队的测试表明，我们的算法可以促进严格的共识，并且在密集的环境中避免障碍物至少为80％。此外，在某些情况下，主动传感系统可以将避免障碍的成功率从50％提高到100％。
This paper proposes a perception-shared and swarm trajectory global optimal (STGO) algorithm fused UAVs formation motion planning framework aided by an active sensing system. First, the point cloud received by each UAV is fit by the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from the received GMM contributes to a global map, which is used as the foundation for consensus. Second, to improve flight safety, an active sensing system is designed to plan the observation angle of each UAV considering the unknown field, overlap of the field of view (FOV), velocity direction and smoothness of yaw rotation, and this planning problem is solved by the distributed particle swarm optimization (DPSO) algorithm. Last, for the formation motion planning, to ensure obstacle avoidance, the formation structure is allowed for affine transformation and is treated as the soft constraint on the control points of the B-spline. Besides, the STGO is introduced to avoid local minima. The combination of GMM communication and STGO guarantees a safe and strict consensus between UAVs. Tests on different formations in the simulation show that our algorithm can contribute to a strict consensus and has a success rate of at least 80% for obstacle avoidance in a dense environment. Besides, the active sensing system can increase the success rate of obstacle avoidance from 50% to 100% in some scenarios.
