There are many industrial, commercial and social applications for multi-agent planning for multirotors such as autonomous agriculture, infrastructure inspection and search and rescue. Thus, improving on the state-of-the-art of multi-agent planning to make it a viable real-world solution is of great benefit. In this work, we propose a new method for multi-agent planning in a static environment that improves our previous work by making it fully online as well as robust to communication latency. The proposed framework generates a global path and a Safe Corridor to avoid static obstacles in an online fashion (generated offline in our previous work). It then generates a time-aware Safe Corridor which takes into account the future positions of other agents to avoid intra-agent collisions. The time-aware Safe Corridor is given with a local reference trajectory to an MIQP (Mixed-Integer Quadratic Problem)/MPC (Model Predictive Control) solver that outputs a safe and optimal trajectory. The planning frequency is adapted to account for communication delays. The proposed method is fully online, real-time, decentralized, and synchronous. It is compared to 3 recent state-of-the-art methods in simulations. It outperforms all methods in robustness and safety as well as flight time. It also outperforms the only other state-of-the-art latency robust method in computation time.
翻译:多旋翼的多智能体规划有许多工业、商业和社会应用,如自主农业、基础设施巡检和搜索救援等。因此,改进多智能体规划的最新技术,使其成为可行的实际解决方案,具有很大的益处。本文提出了一种新的多智能体规划方法,针对静态环境进行改进,使其完全在线,并且具有通信延迟鲁棒性。所提出的框架以在线方式(在我们以前的工作中离线生成)产生全局路径和安全通道,以避免静态障碍物。然后生成一个时态安全通道,考虑到其他智能体的未来位置,以避免智能体内部碰撞。将时态安全通道与本地参考轨迹一起发送到一个 MIQP(混合整数二次规划)/MPC(模型预测控制)求解器中,该求解器输出安全和最优的轨迹。规划频率适应通信延迟。所提出的方法是完全在线、实时、去中心化和同步的。并在仿真中与三种最新的最先进的方法进行比较。它在鲁棒性、安全性以及飞行时间方面都优于所有方法。它还在计算时间方面优于唯一的其他具有最新技术通信延迟鲁棒性方法。