The invention relates to the technical field of unmanned aerial vehicle path planning methods, in particular to an unmanned aerial vehicle path
planning method based on an improved leapfrog
algorithm. According to the method, dynamic
weight adjustment information is obtained by carrying out dynamic
adaptation matching analysis on flight stage information and environment
threat information, a flight stage weight mapping matrix is established, and
fitness function weights are dynamically adjusted according to core targets in take-off,
cruise and landing stages; weight correction is carried out in combination with
environmental data such as obstacle
grid density and a meteorological
threat coefficient, so that the
algorithm focuses on core constraints in the current stage, and target conflicts caused by global unified weight are avoided; according to the method, environmental threats are sensed in real time by means of multi-
sensor fusion, when abnormal states such as over-high obstacle density and strong wind are detected, an
algorithm is triggered to be suddenly stopped and restarted, a
population is reconstructed according to the current position,
threat avoidance weight is enhanced, the defect that an existing mechanism can be updated only after the current iteration period is completed is overcome, and the method is suitable for large-scale popularization and application. And the
response time to an abnormal environment is shortened.