Search Path Planning Algorithm Based on the Probability of Containment Model

Abstract

The location of distress object in the maritime search area is difficult to determine, which has brought great difficulties to the search path planning. Aiming at this problem, a search path planning algorithm based on the probability of containment (POC) model for a distress object is proposed. This algorithm divides the area to be searched into several subareas by grid method and dynamically evaluates the POC of the distress object in each subarea using the Monte Carlo random particle method to build the POC model. On this basis, the POC is dynamically updated by employing the Bayes criterion within the constraint of the time window. Then, the sum of the POC of the object in the subareas is regarded as the weight of the search path. And the proposed algorithm dynamically executes the search path planning according to the maximum path weight. In comparison with the parallel line search path planning algorithm given in the “International Aeronautical and Maritime Search and Rescue Manual,” the simulation results show that the search path planning algorithm based on the POC model of the distress object can effectively improve the search efficiency and the probability of search success of the distress object.

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

“…In SAR missions, most of these coverage path planners mentioned above can be applied; however, it is noticed that they commonly pay no attention to the prior target information, which is available in most SAR cases. Prior target information usually plays a crucial role in designing target search strategies [10,[22][23][24]. Take Ref.…”