Heuristic optimization of multi-agent persistent surveillance revisit time by minimum distance weight functions

Persistent surveillance is one of an important problem in the field of aerial investigation, border patrolling, surveillance and search, Which distinguishes it from other matters by revisit time of major area. The main point of our problem is the minimizing of the time interval between two consecutive hits for a particular area that makes the details and dynamics tracking with higher accuracy. In this paper in order to achieve the minimum revisit time, the heuristic function based on the waypoints time age was used. This function defines the value of each point with the weight functions that optimized by genetic algorithm in several successive iterations. These optimal values are dependent on the position of major point and number of UAV. In multi-agent problems, the patrolling pattern is determined by heuristic weight function in several iterations. Some basic patterns such as lawn mover and spiral are organized to calculate the minimum traveling distance in the search area and obtain the comparative criteria for the results. The paper results lead to improved the navigation during aerial patrolling the area with the graded region , with respect to the UAV dynamics and minimum revisit time.