本文为美国戴顿大学（作者：Megan Clancy Reeves）的硕士论文，共48页。

无人机有着从家用到军用的多种用途。路径规划算法是控制无人机的众多工具之一，可以帮助飞行更加自主，减少飞行员的参与。A*和D*是两种不同的算法，用于解决从业务模式到电路板布局的各种问题。两种算法各有优缺点。A*是更简单的搜索，D*是更复杂的搜索。本文提出了一种新的矩阵实验室路径规划测试环境，比较了算法复杂度和路径长度。通过比较算法复杂度和路径长度，探讨了两种算法的优缺点。通过本文的研究，一种算法可能优于另一种算法，或者将两者的最佳特性结合到一个混合算法中。

Unmanned aerial vehicles (UAVs) have many uses arranging from domestic use to military surveillance. Path planning algorithms are one of the many tools available to control UAVs to help make flights more autonomic and decrease the involvement of a pilot. A* and D* are two different algorithms used to solve a wide range of problems from traffic patterns to circuit board layouts. Both algorithms have both advantages and disadvantages. A* is more of a simple search and D* is a more complex search. This thesis presents a new test environment for path planning in matrix laboratory (MATLAB) to compare algorithm complexity and path lengths. By comparing algorithm complexity and path lengths, the advantages and disadvantages are explored between the two algorithms. By doing this, one algorithm could be a better solution than another or combining the best traits of both into a hybrid algorithm.

1.       引言

2. 路径规划算法：应用于无人机

3. 路径规划算法

4. A*算法

5. D*算法

6. 算法比较

7. 混合算法

8. 结论

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