随着能源与动力技术、飞行器平台技术、智能控制技术、信息传输技术等关键技术的进步，无人机在功能、应用领域方面呈现出多样化发展态势，从室外研究与应用逐步扩展到室内，无人机室内自主飞行与应用研究进入了快速发展阶段，正渐渐成为当前和今后研究的一个热点问题。同大多数无人机相比，四旋翼无人机的优点是结构简易、小巧、操作简便，能够替代人类完成危险性大、灾后救援、室内搜索等各种各样复杂的任务。四旋翼无人机在实际生产应用中，三维航迹规划尤为重要，它直接关系到无人机能否安全的躲避周围环境的障碍物，最终到达目的地。本文围绕四旋翼无人机室内自主飞行问题，提出了效率更高的航迹规划算法，对四旋翼无人机室内三维航迹规划的方法进行具体研究。全文的主要研究工作如下：

       （1）采用栅格方法建立室内三维模型，并分析了无人机搜索方向问题。结合四旋翼无人机机体结构和飞行特点，分析了无人机在室内环境中的飞行条件，研究了航迹规划问题所用的算法，通过对各种航迹规划算法的优缺点进行分析和对比，选择将蚁群算法和Quick-RRT*算法作为算法研究对象。

       （2）针对室内空间复杂的特点且蚁群算法存在初期盲目搜索、收敛速度慢、易陷入局部最优等问题，提出了改进蚁群算法，对初始信息素的设置方法进行改进，增强了蚁群搜索的方向性；设计启发概率，并改进状态转移规则，有效提高了蚁群的可见性精度；优化信息素的更新方式，增加信息素挥发动态调整策略，加快了算法的收敛速度，有效避免其陷入局部最优。并通过实验对算法进行适应性验证，结果表明：优化后的蚁群算法在平均路径长度方面比传统蚁群算法减少38.63%，算法平均用时减少3.80%，且在迭代收敛次数方面也优于传统蚁群算法。为了进一步证明本文改进蚁群算法的可靠性，将其与另外两种改进算法作比较，验证了本文改进算法具有良好的稳定性。

       （3）针对Quick-RRT*算法存在收敛速度慢的问题，将其与改进灰狼算法进行融合。加入人工势场法中的斥力场函数，使规划获得的航迹与障碍物之间保持一定安全距离，确保无人机飞行的安全性，并对所得到的航迹作平滑优化，使得到的航迹符合无人机实际飞行要求。针对无人机在飞行航迹上出现的突发性障碍物，运用局部航迹重新规划策略，对其进行局部重规划处理。实验结果表明：与改进灰狼算法、Quick-RRT*算法相比，提出的Adapted-RRT*算法收敛速度加快，增强了无人机寻找较优航迹的能力和应对突发状况的能力，满足航迹规划的实时性要求。实验对比结果为：Adapted-RRT*算法在航迹长度方面比前两种算法分别降低37.72%、21.53%，用时方面分别减少40.21%、78.21%。

         With the advancement of key technologies such as energy and power technology, aircraft platform technology, intelligent control technology, information transmission technology, drone in the function, application field, has gradually expanded from outdoor research and application to indoors, the autonomous flight and application research in the drone indoors have entered the rapid development stage and is gradually become a hot problem for current and future research. Compared with most drones, the advantages of four-rotorless drones are simple, smaller, easy to operate, and can replace humans to complete a variety of complex tasks such as risk, post-disaster rescue, indoor search, and other complex tasks. In actual production applications, the four-dimensional airplancular planning is especially important. It is directly related to the obstacle to the surrounding environment. This paper surrounds the autonomous flight in the four-rotorless chain, and proposes a higher efficiency of the track planning algorithm, and specific research on the three-dimensional airline planning of four-rotorless drone indoors. The main research work of the full text is as follows:

         (1) The grid method is used to establish an indoor three -dimensional model, and the problem of drone search direction is analyzed. Combined with the structure and flight characteristics of the quadrocriopersy drone, the flying conditions of the drone in the indoor environment were analyzed, the algorithm used in the track planning problems, and the advantages and disadvantages of various airline planning algorithms were analyzed and compared, select an ant colony algorithm and Quick-RRT*algorithm as an algorithm research object.

        (2) For the complex characteristics of indoor space, there is an initial blind search for the ant colony algorithm, the convergence speed is slow, and it is easy to fall into a local optimal problem. It is proposed to improve the ant colony algorithm, and improve the setting method of the initial pheromone, and enhance the ant colony that search direction; design inspiration probability, and improve status transfer rules, effectively improve the visibility accuracy of ant colony; optimize the update of pheromones, increase the pheromone to the dynamic adjustment strategy, accelerate the convergence speed of the algorithm, effectively avoid it follow the topically optimal. And through experiments, the algorithm verification is verified, and the results show that the optimized ant colony algorithm decreases by 38.63%compared with the traditional ant colony algorithm in terms of the average path length, the average use of the algorithm is 3.80%. Ant Colony Algorithm. In order to further prove the reliability of the ant colony algorithm in this article, compare it with the other two improved algorithms, and verify that the improvement algorithm of this article has good stability.

        (3) The problem of slow convergence at the Quick-RRT* algorithm is integrated with the improvement of gray wolf algorithm. Join the scold field function in the artificial potential field method to keep a certain safe distance between the planned trails and obstacles, ensure the safety of drone flight, and make smooth optimization of the trails obtained to make the obtained, the trails meet the actual flight requirements of the drone. Aiming at the sudden obstacles of drones on the flight trail, the local aircraft track re -planning strategy is used to carry out local re -planning treatment. The experimental results show that compared with the improvement of the gray wolf algorithm and the Quick-RRT* algorithm, the Adapted-RRT* algorithm convergence is accelerated, which enhances the ability of drones to find better trains and the ability to respond to emergencies, and satisfy satisfaction. Real-time requirements for flights planning. The experimental comparison results are: Adapted-RRT* algorithm decreases by 37.72%and 21.53%compared with the previous two algorithms, respectively, and the time is reduced by 40.21%, 78.21%, respectively.

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