Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method

The International Conference on Maritime Search and Rescue, in April 1979, concerned the establishment of an international maritime search and rescue (SAR) plan covering the needs for ship reporting systems, SAR services and the rescue of persons in distress at sea. Included in the publication are:Final Act of the ConferenceInternational Convention on Maritime Search and Rescue (SAR), 1979Resolutions adopted by the ConferenceThis edition includes amendments to the International Convention on SAR which were adopted by resolution MSC.155(78) in May 2004. These amendments came into force on 1 July 2006.

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR.

In maritime search and rescue (SAR) missions, search path planning is a key factor in determining rescue efficiency and success rate. Due to the limitations of traditional algorithms – which rely heavily on manual decision-making, are strongly subjective, and struggle to handle complex environments – this paper proposes a maritime SAR path planning method based on an improved Deep Q-Learning (DQN) approach. First, a marine SAR environment model is constructed to accurately predict the drift trajectory of distressed targets. Then, a maritime SAR strategy using deep reinforcement learning is developed, enabling global optimization of the search path through action and state space simplification, along with a multi-condition constraint reward function. A hybrid exploration rate adjustment strategy and a semi-gradient strategy are designed to improve training efficiency. Model stability is enhanced through the combination of soft target network updates and a prioritized experience replay mechanism. Simulation results demonstrate that the model possesses strong multi-objective optimization capabilities, achieves complete coverage of the effective area with minimal step size, and automatically avoids obstacles with a zero repeated search rate. This provides a valuable reference for the development of maritime SAR decision-making strategies.

Unmanned aerial vehicles (UAV) are a promising technology for fast, large scale survey operations such as maritime search and rescue (SAR). However, providing reliable communications over long distances remains a challenge. Cellular technology such as Long Term Evolution (LTE) is designed to perform well in the presence of fast-moving clients and highly dynamic channel conditions. Yet, its generic medium access mechanisms make it hard to support low-latency uplink traffic, which is needed for real-time command and control and similar applications. In this paper, we introduce an unmanned aerial system for maritime SAR, supported via an LTE data link. To mitigate drawbacks in uplink channel access, we develop a resource-guaranteed scheme based on persistent scheduling, using an open-source LTE stack. The approach is evaluated with a laboratory setup using software-defined radio modules. Measurements are taken on the application layer, using realworld telemetry data generated by an autopilot system in the presence of high bandwidth background traffic. Evaluations demonstrate that our system fulfills UAV requirements in terms of latency and reliability. Latency is further reduced to 6 ms on average and a 99.9th percentile of 10 ms by application-aware overprovisioning for mission-critical traffic and users.

The development of maritime trade and operations has led to a gradual increase in maritime accidents. Research on the specificity of maritime search and rescue (SAR) missions is essential to improve mission efficiency; however, traditional SAR programs usually use predefined paths to conduct searches, which is difficult to meet the timeliness and uncertainty requirements. To solve the challenge, we propose a Boundary Adaptive Neural Network Coverage Path Planning Scheme based on Target Drift Prediction (BANCP‐TDP). The framework includes three modules: drift trajectory prediction, optimal region determination, and coverage search. First, the Limited Red‐billed Blue Magpie Optimizer Back Propagation drift model is used to predict the drift trajectory of the wrecked target. Subsequently, we use the Multiphysics Monte Carlo Gravitational Search prediction model to determine the distribution of targets at different moments and the optimal SAR region for guiding an autonomous underwater vehicle to carry out SAR missions. Then, we propose a BANCP for the SAR regions with complex boundaries, aiming to minimize the path length and maximize the coverage ratio. The comparative field experiments at Qingdao Jin Cao Gou reservoir and simulation results show that the proposed framework can effectively shorten path length while minimizing the repeated paths.

Maritime Search and Rescue (SAR) operation is a critical process which aims to minimize the loss of life, injury, material damage by rendering aid to persons in distress or imminent danger at sea. Response time to incidents is the most crucial performance metric in SAR operations. Fatalities, injuries and damage can be reduced if incidents are responded as quickly as possible. However, uncertainty of location, time and severity of an incident make these operations difficult to conduct and complicated to plan. Furthermore, quick response relates to rational planning and allocation as well as the use of modern and fast SAR resources. In this paper, we combine optimization and simulation methodologies and study the problem of allocating SAR helicopters. Our methodology works in two stages; we first determine an optimal allocation scheme of helicopters with the objective of minimizing the average response time to incidents in the responsibility region. Next, we use a discrete event simulation model to test the performance of our analytical solution under stochastic demand. Using 2014 incident data of the Aegean Sea, we demonstrate a case study in the western sea region of Turkey.

Successful maritime search and rescue (SAR) missions and real-time information exchange among first responder organizations (FRs) and command/coordination centers, fully rely on straightforward information flow and accurate communication channels, supported by novel data sharing technologies, advanced network connections, and interoperable platforms. Therefore, SAR actions at sea, as an integral part of the national maritime safety system, are supported with mission-critical communication (MCC) networks and devices. The paper presents the analysis of SAR operational and communication specifics, use cases and technical efficiency increasing in the maritime safety framework, with a focus on specific operational capabilities of MCC, connection networks, devices, and procedures for maritime distress communication. Further to MCC, the paper provides insight into the 5G network applied in maritime SAR and communications. The case study consists of an elaboration of the Mission Critical Communication platform components and X/BELLO tool, a secure 5G application that allows real-time information exchange, deployed under the RESPOND-A project approach. These systems represent the technical part of the SAR pilot of the EU RESPOND-A project showcasing the opportunities to strengthen the operational framework of maritime FRs and improve their joint mission performance.

Despite the significant increase in maritime activities in recent years in the Barents Sea, maritime search and rescue (SAR) modelling and simulation in the Arctic waters, including in the Barents Sea is a relatively less researched area.The present study proposes an agent-based modelling framework, which models and simulates different SAR scenarios and evaluates the performance of the SAR system under various operating conditions, while considering the dynamicity and uncertainties of the weather and sea conditions.

This paper presents a solution to the problem of pattern formation on a grid map, for a homogeneous multi-robot system. In this paper we propose a natural swarm inspired algorithm based on the Particle Swarm Optimization (PSO) model and Virtual Pheromone mechanism. Basically, a virtual pheromone trail based method is proposed as the message passing mechanism among the robots, where robots make distributed movement decisions through local interactions. For one individual robot, there are two working modes, exploration and dispersion, with different indicators in the PSO model. By cooperating and communicating through the virtual pheromone, agents of the multi-robot system switch between the two phases. The PSO method helps to allocate reasonable robots to different parts of the predefined pattern. A series of experiments on simulator is carried out and proves the convergence and excellent scalability of our algorithm. By optimizing some parameter in the PSO model with the help of the simulator, the efficiency of pattern formation is further improved.

Epidemiology of Search and Rescue in Baxter State Park: Dangers of Descent and Fatigue

Unmanned Aerial Vehicles (UAVs) can be an important resource when performing Search and Rescue (SAR) operations at sea, as this technology is fairly inexpensive when compared to traditional SAR approaches that use significant human resources and expensive air and naval assets, thus enabling the deployment of several UAVs simultaneously in these missions to perform rescue targets search in maritime environments.In order to maximize the usefulness of these UAVs in such operations, we propose a method which utilizes a state-of-the-art object detection network to perform real-time rescue target detection on-board the UAV, using standard RGB cameras, with minimal human intervention, thus enabling an increased vehicle autonomy and search range. Additionally, since the UAVs only relay the candidate images and locations that contain possible rescue targets, given by the onboard detector, it is possible to have several UAVs working in parallel that report back to a single human operator.We have selected the YOLOv4-tiny detection network, pretrained in the COCO dataset, and retrained it to detect rescue targets at sea. For this purpose some datasets were recorded and annotated to simulate the presence of maritime rescue targets. The proposed approach has been validated on an independent test dataset, showing that it has good detection capabilities and thus providing convincing results regarding the use of UAVs with automatic target detection capabilities in SAR missions.

Search and Rescue (SAR) is an operation conducted to find and return to safety the survivors of an emergency incident, where the search may have to be conducted in difficult areas such as in mountains, deserts, forests or at sea. Maritime SAR operations normally involve a large number of vessels and possibly helicopters as well, but this has been found not to be effective enough. This paper looks at Unmanned Aerial Vehicles (UAVs) to assist in maritime SAR operations, leaning towards rotary wing UAVs with the capabilities of flying vertically and horizontally as well as hover at required positions for close monitoring. The focus is on the algorithm for determining the exact position of the target subject(s).

Particle swarm optimization (PSO) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the PSO continuous model and to introduce different PSO family members including the generalized PSO (GPSO) algorithm, which is the generalization of PSO for any time discretization step. In this paper, we present the stochastic analysis of the linear continuous and generalized PSO models for the case of a stochastic center of attraction. Analysis of the GPSO second order trajectories is performed and clarifies the roles of the PSO parameters and that of the cost function through the algorithm execution: while the PSO parameters mainly control the eigenvalues of the dynamical systems involved, the mean trajectory of the center of attraction and its covariance functions with the trajectories and their derivatives (or the trajectories in the near past) act as forcing terms to update first and second order trajectories. The similarity between the oscillation center dynamics observed for different kinds of benchmark functions might explain the PSO success for a broad range of optimization problems. Finally, a comparison between real simulations and the linear continuous PSO and GPSO models is shown. As expected, the GPSO tends to the continuous PSO when time step approaches zero. Both models account fairly well for the dynamics (first and second order moments) observed in real runs. This analysis constitutes so far the most realistic attempt to better understand and approach the real PSO dynamics from a stochastic point of view.

Unmanned Aircraft Systems (UAS/Drones) are great resources for search and rescue (SAR) operations as they allow for remote examination of vast areas. This paper presents a smart human subject localization system called Project Vulture. Our system utilizes a trained deep learning model to speed up the analysis of obtained drone aerial images for scalable and distributed operations. Given that SAR deals with human lives, our localization model focuses on obtaining higher sensitivity than existing peer models. In this situation, we accept the possibility of a higher false positive rate. Our obtained results are promising and encourage continued research in this field.

The main objective of this paper is to solve the inverse convection heat transfer problems with particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortcoming of earlier convergence of standard PSO algorithms. The performance of EPSO is tested by some benchmark functions; it is shown that EPSO has a strong antilocal trap capability especially for high dimensional multimodal optimization problems. At last, EPSO is used to identify the unknown boundary heat flux in a channel flow. According to the computational results of four test problems, it is clear that the proposed EPSO algorithm is able to estimate the unknown heat flux accurately even when the input data contain measurement error.