基于改进人工势场法的无人机路径规划算法

针对传统的人工势场(APF)法无法适应复杂环境而陷入局部停滞状态、路径不够平滑等不足,提出了改进的人工势场法。首先,该算法对威胁的连通性进行分析,借鉴几何拓扑学思想得到可行解域。其次,该算法在可行解域内进行航迹点预规划。预规划基于威胁分布的全局性信息,弥补人工势场法易陷入局部最小而无法找到可行路径的不足。最后,该算法改进人工势场法引力函数,通过多次迭代,并进行曲率检查以获得足够平滑的可飞路径。仿真结果表明改进算法能够满足无人机路径规划的要求,且简便可行,具有较强寻优能力及适应性。

Path planning algorithm for unmanned aerial vehicles based on improved artificial potential field

There are still some issues existing in the traditional Artificial Potential Field (APF), such as the poor adaptability to the complex environment, easily getting into local standstill and the unsmooth path. In order to solve these problems, an improved artificial potential field method was proposed. Firstly, the connectivity of threats was analyzed by the proposed algorithm, and the optimum feasible solution domain was got by the geometric topology. Secondly, a pre-planning of track points was carried out within the feasible solution domain. The pre-planning was based on the threats' global distribution information, and made up for the deficiencies of falling into local minimum and failing to find a feasible path. Finally, the gravitational function of artificial potential field method was improved, and a sufficient smooth flight path was obtained by several iterations and curvature checking. The simulation results show that the improved algorithm can meet the path planning requirements of unmanned aerial vehicles. The proposed algorithm is simple and feasible with strong searching and adaptability.