论文标题
自我劳动：在混乱的环境中，完全自主和分散的四极管群系统
EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments
论文作者
论文摘要
本文提出了一种分散的异步系统解决方案，用于在未知的障碍物丰富的场景中使用仅使用板载资源的多机自主导航。规划系统是在基于梯度的本地计划框架下制定的，在该框架中，通过将碰撞风险作为非线性优化问题的惩罚来实现避免碰撞。为了提高鲁棒性并逃脱局部最小值，我们结合了一种轻巧的拓扑轨迹生成方法。然后，使用不可靠的轨迹共享网络仅在几毫秒内生成安全，平滑且动态可行的轨迹。通过在深度图像中使用代理检测来校正代理之间的相对定位漂移。在模拟和现实世界实验中都证明了我们的方法。源代码发布以供社区参考。
This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.
