this paper, we mainly focus on how researchers have been addressing these challenges in optimal motion planning to obtain robust navigation in various domains for multirotor aerial vehicles (MAVs). In most industrial applications, the environment is either fully or partially unexplored, and unpredictable events can occur at any time due to a variety of factors. Therefore, a fast and accurate optimal motion planning technique is required to handle these unexpected problems in real time. The optimal motion planning problem is generally divided into three subcategories: path planning followed by smoothing, kinodynamic search-based trajectory generation, and motion-primitive-based approaches. Of these three, plan-based control approaches are the most widely used and efficient way to address the problem considered, compared to the other two approaches. Numerous plan-based control strategies have been proposed in the past decade, with promising results. This is one of the key motivating factors for reviewing plan-based control, especially for industrial multirotor aerial vehicles (MAVs). Most industrial multirotor aerial vehicles (MAVs), such as quadrotors, have low-level controllers, for example, PX4 [1], DJI [2], that can be operated independently irrespective of high-level execution commands. Moreover, such controllers reduce the overhead and complexity of developing high-level planning algorithms due to their independence. In other words, the same planner can be deployed on different firmware by implementing an interface between a high-level planner and a low-level controller. Thus, we narrowed down our study to considering only plan-based control approaches (Figure 1), particularly in application to industrial multirotor aerial vehicles (MAVs). The main limitation of multirotor aerial vehicles (MAVs) is low flight time. Hence, a multirotor aerial vehicle (MAV) should be capable of executing robust, agile, and aggressive maneuvers while ensuring dynamic feasibility and guaranteeing smoothness of the trajectory in low flight time. Furthermore, trajectory plotting should be performed within an obstacle-free zone at high speed to handle a given mission effectively. Such behavior is imposed by adhering to a set of constraints. If and only if the constraints are incorporated appropriately, desired needs can be fulfilled. Obtaining the right constraints at the right moment and applying appropriate control sequences to improve motion quality is the key objective of any plan-based control approach. However, the procedure of obtaining such right constraints is an open research problem due to its complexity and numerous other challenges that should be handled simultaneously. For example, the multirotor aerial  
Figure 1: An overview of the plan-based control paradigm in the context of trajectory planning problem formulation. There are various ways to formulate the trajectory planning problem, each of which consists of a set of submodules (green color boxes) depending on the problem behavior. vehicle (MAV) has been widely employed in video-making-related fields in recent years, with cinematographic aerial shooting being one of the popular areas of interest during the last five years. In such shooting, generating smooth, obstacle-free trajectories is the main challenge. Various other challenges exist, and most of them are application-specific. In this work, we examine the most common problems related to trajectory planning applications in the paradigm of plan-based control, as well as how researchers have been alleviating those problems by proposing compelling solutions. In optimal trajectory planning, trajectory generation and controlling the multirotor aerial vehicle are strongly interconnected. For multirotor aerial vehicles (MAVs), the trajectory generation process is relatively easy due to the dynamic properties of the multirotor aerial vehicles (MAVs). When dynamic obstacles are incorporated, the trajectory has to be refined at a high rate in order to keep a smooth maneuver despite increased computational demands.