In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles (MAVs), impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.

翻译：通常，最优运动规划既可以在局部进行，也可以在全局进行。在这类规划中，选择局部规划还是全局规划技术主要取决于环境条件是动态还是静态。因此，最合适的选择是使用局部规划，或局部规划与全局规划相结合。在设计局部和全局最优运动规划时，需牢记的关键指标是执行时间、渐近最优性以及对动态障碍物的快速反应。与路径规划后平滑等其他方法相比，此类规划方法能更高效地满足上述目标指标。因此，本研究的主要目标是分析相关文献，以理解当为多旋翼飞行器实时生成最优轨迹时，运动规划（特别是轨迹规划）问题的表述方式如何影响上述所列指标。研究结果将轨迹规划问题分解为一组子问题，并识别并详细描述了解决每个问题的方法列表。随后，总结了2010年至2022年间最突出的研究成果，并以时间线的形式进行了呈现。