The present invention discloses a target search
route planning method based on cumulative probability optimization. The method comprises the following steps: 1) selecting multiple search formations suitable for the search task from a formation
route type
library, then delineating a grid to generate an
effective solution space based on the total number of search platforms in the multiple search formations, the target search range required to complete the given task, the
turning radius of the search platform, and the detection range; 2) obtaining the detection probability
density distribution of the search platform; 3) obtaining the probability
density distribution of the target; 4) calculating the time-varying detection probability of each
waypoint in the i-th solution space; and 5) obtaining a target search
route planning scheme based on a specified time and an integrated cumulative probability weight according to a priority index F. The present invention utilizes an effective planning strategy solution space and an integrated
probability model to rapidly obtain a strategy suitable for an unmanned surface search platform to perform the task, thereby ensuring a high cumulative detection probability and simplifying the
search procedure of the unmanned surface search platform.