## 1 _Applied_  
### _Acticle_  
Modified A-Star (A*) Approach to Plan the Motion of a Quadrotor UAV in Three-Dimensional Obstacle-Cluttered Environment  
[1] [PERSON] 1, [PERSON] 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33  
weapons, which can search for a specific target by recognizing the facial features or other prescribed characteristics of the target. Due to its vertical takeoff and landing (VTOL), perching ability, and quasi-stationary flight attributes, the quadrotor UAV has garnered much attention from the research community as compared to other UAVs [7,8]. For any robot, the central motive for novelties and research expansions is to bring about ultimate autonomy and intelligence. Therefore, during the past several years, researchers have contributed significantly in the fields of design, intelligent control, trajectory generation, and motion-planning to enhance the autonomy level of micro quadrotor UAVs [9, 10, 11, 12, 13, 14, 15]. Motion-planning has widely been investigated for robots operating in various kinds of environments, and several methods exist to search for a path in obstacle-cluttered environments [16, 17, 18, 19, 20]. Dijkstra is an earlier and simpler version developed to find the shortest path on a map. Later, it was improved, and the subsequently developed algorithm is known as a greedy best-first search algorithm, which works in a similar fashion as does Dijkstra. However, the latter incorporates guided search through heuristics, which helps in estimating how far the goal is from any current vertex. The Dijkstra algorithm works in all directions simultaneously and is therefore computationally costly for three-dimensional motion-planning. However, it is guaranteed to find the shortest path. Contrarily, greedy best-first search algorithm works in a guided direction and runs quickly. However, the path found is not guaranteed to be the shortest. Therefore, it only focuses on the cost to reach the goal point and ignores the cost of the path, which is the major drawback of this algorithm. The promising solution is the combination of both aforementioned algorithms, and the consequence is known as A* algorithm, i.e., A-star. A* is the most popular algorithm in pathfinding problems, and it has been used extensively in numerous ground robotic path search applications [17, 18, 19, 20, 21, 22]. Extension of A* to 3D space poses the issue of exploration of extra nodes that result in a suboptimal path. Such suboptimal paths will render a trajectory that increases the control effort of the UAV and obviously reduces the flight endurance. Autonomous navigation of a micro quadrotor UAV through obstacles cluttered in a three-dimensional space requires fast search algorithms featuring the ability of map-reading in order to generate a shortest collision-free path between initial and final positions. The generated shortest path must be dynamically feasible, smooth, and optimal [23, 24, 25, 26, 27, 28]. A typical motion-planning problem involves two entities; one is the determination of the shortest collision-free path between start and final goal positions using the search algorithm, and the other is the generation of a dynamically feasible trajectory by taking into account the found shortest path [29, 30, 31]. Most of the commonly used search algorithms for solving autonomous robot navigation problems are graph-theory-based, and the generated shortest path always contains infinite curvatures [32, 33, 34, 35]. This ultimately implies certain unwanted limitations on quadrotor mission profile, i.e., quadrotor dynamics cannot handle infinite curvatures and it must stop at these points. To eradicate this limitation in our work, the shortest path found by the search algorithm is handed over to a trajectory generation algorithm, which generates a dynamically feasible and optimal trajectory with finite and smooth curvatures.