Cooperative MASS path planning for marine man overboard search. (1st September 2021)

• Record Type: Journal Article
• Authors: Mou, Junmin; Hu, Tao; Chen, Pengfei; Chen, Linying
• Is Part Of: Ocean engineering. Volume 235(2021)
• Publication Date: 2021-09-01
• Subjects: Cooperative path planning -- Man overboard -- Multiple MASS -- Adaptive greedy algorithm -- Search and rescue; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.109376
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: In a Man Overboard (MOB) incident, a quick and effective Search and Rescue (SAR) operation is crucial to increase the survival probability of the victim. Determining the search area and planning paths for the rescue ships are essential for efficient SAR operation. The search area's determination requires the prediction of the missing person, facing the challenges of lacking information about the accurate position and time of falling and the influence of environmental disturbances. Two main aims of path planning for SAR operation are quick arrival at the search area and coverage search with high cumulative Possibility of Success (POS). Many path planning algorithms have been proposed. Most of them aim at finding the shortest paths, which meet the goal of quick arrival. However, the path planning for maximizing POS of finding the person is still lacking. Besides, compared to an individual ship, a fleet of cooperative Maritime Autonomous Surface Ships (MASS) can significantly increase the POS and reduce SAR personnel's risk in a time-sensitive SAR operation. Therefore, in this paper, we propose a cooperative path planning framework to search for the missing person in a MOB incident using a fleet of fully autonomous MASS. The framework is divided into three modules, i.e., position prediction, target tracking, and coverage search. Firstly, the stochastic particle simulation method is used to predict the missing person's position considering the environment forecasting data, which determines the search area. Secondly, an adaptive greedy search algorithm is applied to tracking the drifting predicted area. Thirdly, the coverage search algorithm is designed with an adaptive neighborhood and evaluation function for increasing the cumulative POS in a limited time. Moreover, the path is smoothed by the Line-of-Sight algorithm and the kinematic interpolation method. Simulation experiments and sensitivity analysis are carried out to demonstrate the effectiveness of the proposed framework. Highlights : Cooperative path planning framework is proposed for multiple MASS to carry out the MOB search. . The stochastic particle simulation is applied to generate the probability map of the MOB. . An adaptive greedy algorithm is proposed for maximizing the cumulative POS of the MOB in a limited time.