This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.

翻译：本文介绍了在有未知障碍的室内环境中进行在线轨迹规划的两步算法。 在第一步,基于抽样的路径规划技术,如最佳快速探索随机树(RRT*)算法和视觉线算法(LOS)算法,被用于产生由多个路径点组成的无碰撞路径。然后,在第二步,受限制的二次编程用于计算穿越所有计算路径点的平稳轨迹。这项工作的主要贡献是开发一个灵活的轨迹规划框架,能够探测环境的变化,例如新的障碍,并实时计算替代轨迹。拟议的算法积极考虑环境的所有变化,并仅在有新障碍的行进点进行再规划过程。这有助于减少计算时间,在实时实现拟议方法。在模拟和现实世界实验中,利用Intel Aero Stread-Fly(RTF)的二次审校来评估拟议算法的可行性。