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.
Highlights
Search and rescue of maritime distress target is a high-risk, difficult, time-sensitive, and professional task, and it is the only way to search and rescue survivors [1]
Aiming at the above problems, this paper proposes a search path planning algorithm based on the probability of distress target inclusion
As a result, based on the search task planned by the probability of containment (POC) model of the distress target at time t, the search platform needs complete searching in the search area Gt within the time window (t, t + T), where T is the search time window and its value is related to the drift velocity of the distress target VT and the length of the search subarea d
Summary
Search and rescue of maritime distress target is a high-risk, difficult, time-sensitive, and professional task, and it is the only way to search and rescue survivors [1]. It is difficult to fully consider the potential relationship between many factors affecting the drift process of the target in distress by expert experience alone, and the accuracy is low, which affects the success rate of search and rescue For this reason, the researchers used statistical and stochastic theory frameworks to express the drift model of the distress target and used machine learning method to estimate the drift trajectory [6,7,8]. E paper [17] regarded the search path planning task as a multiconstrained objective optimization problem based on fully considering the dynamic update of the probability distribution map of the distress target location, and the improved particle swarm optimization algorithm was used to realize the dynamic planning of the search path.
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