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：本文提出了一种用于未知障碍物室内环境中在线轨迹规划的两步算法。第一步采用基于采样的路径规划技术，例如最优快速探索随机树（RRT*）算法和视线（LOS）算法，生成由多个航点构成的无碰撞路径。第二步利用约束二次规划计算一条穿过所有航点的平滑轨迹。本研究的主要贡献在于开发了一个灵活的轨迹规划框架，该框架能够检测环境变化（如新增障碍物）并实时计算替代轨迹。所提算法主动考虑环境中的所有变化，仅对受新障碍物占据的航点执行重新规划过程。这有助于减少计算时间并实现实时应用。通过Intel Aero Ready-to-Fly（RTF）四旋翼无人机在仿真和真实实验中的验证，评估了所提算法的可行性。