多无人机在障碍物密集区实现稳定通信的高效部署与实时协同
Multi-UAV Deployment in Obstacle-Cluttered Environments with LOS Connectivity
- 基于最小边RRT*算法设计少无人机的树状中继拓扑
- 仿真与实测验证了3D峡谷环境中全时线性视距连通性
- 适合需要动态自适应通信网络的无人机协同场景
在障碍物密集环境中,多无人机可靠通信至关重要,因遮挡常导致通信受限。常见方案是部署中继无人机构建多跳网络,但面临两大挑战:如何设计多跳网络结构,以及如何在协同运动中保持连通性。本文提出一种基于最小边RRT*的高效约束搜索方法,以最少无人机数实现覆盖树拓扑部署;进一步设计分布式模型预测控制策略,用于在线运动协调,显式集成无人机间、无人机-障碍物距离约束及视线(LOS)连通性约束。这些约束通常非线性且需近似处理,而本文提供理论保证:所有代理轨迹全程无碰撞,团队始终维持线性视距连通。在三维山谷环境进行大量仿真,并通过硬件实验验证了部署位置在线变化时的动态适应能力。
原文摘要 · Abstract (English)
A reliable communication network is essential for multiple UAVs operating within obstacle-cluttered environments, where limited communication due to obstructions often occurs. A common solution is to deploy intermediate UAVs to relay information via a multi-hop network, which introduces two challenges: (i) how to design the structure of multihop networks; and (ii) how to maintain connectivity during collaborative motion. To this end, this work first proposes an efficient constrained search method based on the minimumedge RRT? algorithm, to find a spanning-tree topology that requires a less number of UAVs for the deployment task. Then, to achieve this deployment, a distributed model predictive control strategy is proposed for the online motion coordination. It explicitly incorporates not only the inter-UAV and UAVobstacle distance constraints, but also the line-of-sight (LOS) connectivity constraint. These constraints are well-known to be nonlinear and often tackled by various approximations. In contrast, this work provides a theoretical guarantee that all agent trajectories are ensured to be collision-free with a teamwise LOS connectivity at all time. Numerous simulations are performed in 3D valley-like environments, while hardware experiments validate its dynamic adaptation when the deployment position changes online.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。