Patent ID: 11938636
Assignee: NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS
Field: Measurement (Instruments)
Classification: CPC B  G  Y | IPC B  G

Claim 4:
5. The feature-guided scanning trajectory optimization method of claim 1, wherein the step (S4) comprises:
(S41) determining a starting feature to be measured S and a target feature to be measured D, and initializing a pheromone, a search path and a tabu list of individual feature nodes to be measured to build a 3D working environment of the aircraft surface;
(S42) placing M ants at the starting feature to be measured S; placing the starting feature to be measured S into the tabu list; calculating a state transfer probability of each of the M ants for individual feature nodes to be measured in a next plane; and randomly selecting a next feature node to be measured by using a roulette wheel selection method;
(S43) determining whether the target feature to be measured D has been reached; completing a search after all ants reach the target feature to be measured D;
arranging lengths of crawling paths of all ants in an ascending order; and updating the number of ants and a pheromone value of individual feature nodes to be measured on a corresponding path;
(S44) setting the number of updates v; if the global optimal scanning trajectory does not change after consecutive v iterations, adjusting a pheromone volatilization factor, and then returning to the step (S42), expressed as:, τ
   ⁡
   (
   N
   )
  
  =
  
   {
   
    
     
      
       
        
         
          0
          .
          9
         
         ×
         
          τ
          ⁡
          (
          
           N
           -
           1
          
          )
         
        
        ,
        
         
          τ
          ⁡
          (
          N
          )
         
         >
         
          τ
          min
         
        
       
      
     
     
      
       
        
         τ
         min
        
        ,
        
         
          τ
          ⁡
          (
          N
          )
         
         ≤
         
          τ
          min
         
        
       
      
     
    
    ,
   
  
 

wherein N is a current iteration number; τ(N) represents a pheromone volatilization factor of a Nth iteration; and τmin represents a minimum pheromone; and
(S45) determining whether the maximum number of iterations has been reached; if yes, outputting a length of the global optimal scanning trajectory; otherwise, clearing the tabu list, and then returning to the step (S42).