Quick and efficient mission planning is essential in maritime search and rescue (SAR). This includes defining the search area and developing an effective strategy. The task is fraught with challenges due to the difficulty of determining location information and the impact of complex meteorological environments. The primary objective of SAR mission planning is the rapid deployment of unmanned surface vehicles (USVs) to the incident area. While many planning algorithms prioritize the shortest route, there’s a lack of mission planning measures that maximize SAR effectiveness. In addition, the joint deployment of USVs increases the success rate compared to individual operations. Therefore, this paper presents a task assignment framework for USVs in SAR missions that considers the probability of success and time constraints. USVs are used to search for lost targets, and the framework consists of the following three modules: (1) a module for predicting the location of the overboard target to be rescued; (2) a module for modeling the probability of mission success; (3) a module for assigning search tasks to USVs. The framework first analyzes the search area. Then, it predicts the target location with a stochastic particle method, which incorporates marine environment forecast data to update the mission target location. To improve the scientific nature of USV search and rescue mission plans, an evaluation model is developed to assess mission capability. Simulation experiments and task scheme analysis validate its effectiveness.

The research on emergency response systems usually only considers the role of professional rescuers in emergency rescue or the activities taken by the victims in the process of self‐rescue, and the joint research of the victims and rescuers in the same environment is relatively few. Multiagent modeling technology is a promising tool for simulating natural disaster emergency response systems. Based on the background of the earthquake rescue operation, this paper studies the related contents of the cooperative emergency response system of disaster victims and rescuers by using the modeling and simulation method of the multiagent. On the premise of making full use of the victims’ ability to obtain information and move, it is proposed that the rescuers and the victims move together to the cluster point to complete the treatment to reduce the time wasted by the rescuers moving among the victims. This paper simulates different degrees of disaster through many experiments and simulates the influence of the relative speed of victims and rescuers by changing the moving speed of victims. It can be found that when there are many disaster victims, the collective rescue operation can reduce the overall emergency response time; when the movement speed of the victims is lower than that of the rescuers, the effect of the clustered rescue is similar to that of the victims waiting for rescue. When the movement speed of the victims is similar to that of the rescuers, the clustered emergency response is better than that of the victims waiting.

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperative area search in uncertain battlefield environment, a multi-UAVs cooperative area search algorithm based on co-evolutionary genetic algorithm (CEGA) is proposed. Firstly, the raster Map and extended Search Probability Map (SPM) model of the task area are constructed, the UAV state model and sensor detection model are established, and the search environment updating method based on the Bayesian criterion is presented. Then, considering the cooperative elements of multi-UAVs, the cooperative area search planning model is established, and based on the rolling optimization idea, the co-evolutionary genetic algorithm with elite retention strategy is used to optimize the solution, and the multi-UAVs cooperative search paths are generated. Through simulation experiments and comparative analysis, the effectiveness of the proposed algorithm is verified.