A maritime search and rescue path planning method based on improved DQN

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.

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.

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).

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.

Maritime search and rescue (MSR), as the last defense line for the life at sea, plays a great role in shipping industry and ocean environment protection. MSR is attracting more and more attention from technical and scientific aspects which are expected to help locate the victim goals quickly, precisely, and efficiently. So far, there have been some new techniques applied to MSR, such as computer vision. However, these techniques have a common drawback that they are too dependent on the effort of the search and rescue party, so that the victim goals can only passively wait for their search. To change this kind of passive status, wireless sensor network (WSN) is the deficiencies of the current maritime search and rescue. To make WSN localization suitable for the MSR, an improved microelectromechanical systems MEMS aided algorithm on the basis of triangle and centroid algorithm is proposed to locate and track the search targets in real time and more precisely. Finally, the simulation results show that the proposed method and algorithm are feasible and efficient.

To solve path planning problem of continuous space-time Markov moving targets for UUV search, an optimal path planning model is established. The search direction of the UUV is set as decision variables. An improved genetic algorithm is adopted to pursue an optimal path for underwater anti-submarine search. The algorithm utilizes an improved real number encoding method to describe the path. The target’s motion is assumed as uniform distribution in direction and normal distribution in velocity around an initial speed. The results show that the search path planning is more reasonable through a certain number of genetic and cross mutation operations. The proposed method has the advantages of high search efficiency, good stability and short reaction period, and is suitable for solving underwater path-searching problems.

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.

Malaysian search and rescue (SAR) operations play a critical role in ensuring the safety and security of citizens, visitors and assets. Due to the extensive coastline and air traffic, effective SAR operations are essential in protecting lives and properties. The current study aims to improve the effectiveness and efficiency of maritime and aeronautical SAR operations in the Malaysian SAR region by enhancing the collaboration between authorities and developing a solid organisational basis. Human resources, SAR resources, training and logistic support are emphasised. This study employed a quantitative methodology and recruited 140 personnel from SAR agencies. Empirical data were collected through a questionnaire with a simple random technique. Resultantly, training possessed the strongest association with organisational performance. The findings could provide insights into improving operational performance during SAR crises and facilitate strategic decision-making, which would also be instrumental to other government agencies conducting similar research.

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.

This paper addresses the problem of path planning and task allocation for cooperative search and rescue (SAR) missions of unmanned boats in a typical SAR scenario. Firstly, a system model for cooperative SAR of unmanned boats is established, including environmental model, task model, unmanned boat model, and task model, to the complex maritime SAR environment and task requirements. Then, the task allocation process is divided into two core stages, which are applied to the cooperative SAR mission of unmanned boats Through simulation experiments, the performance of BLSA algorithm and other intelligent optimization algorithms in terms of search and rescue efficiency, path length, and computation time is compared and. The results show that the BLSA algorithm has significant advantages in solving the cooperative SAR problem of unmanned boats, which can improve the search and rescue efficiency, shorten search and rescue time, and provide new solutions for maritime rescue tasks.

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.

Frequent natural disasters and man-made catastrophes have threatened the safety of citizens and have attracted much more attention. The rescue mission under disaster environment is very complicated and dangerous for a rescue team. Search and rescue (SAR) robots can not only improve the efficiency of rescue operations but also reduce the casualty of rescuers. Robots can help rescue teams and even replace rescuers to perform dangerous missions. Search and rescue robots will play a more and more important role in the rescue operations. A survey of the research status of search and rescue robots in Japan, USA, China and other countries has been provided. According to current research, experiences and the lessons learned from applications, the five key performances of a search and rescue robot are survivability, mobility, sensing, communicability and operability. Multi-technique fusion and multi-agent intelligent network are considered to be requirements for the future development of the search and rescue robot. Disaster prevention, disaster reduction and disaster rescue are the important parts of national public safety. They are also crucial for the safety of citizens and their estates. Search and rescue robotic technique is an urgent needed, strategic and core technique for national development. It will be important and strategic for national economy and safety.

Unmanned Aerial Vehicles (UAVs) play a crucial role in Maritime Search and Rescue (MSAR), contributing to the improvement of rescue efficiency and reduction of casualties. Typically, UAVs equipped with cameras collect data from disaster areas and transmit it to the shore-based rescue command centers. By deploying Mobile Edge Computing (MEC) servers, UAVs can pre-process video footage to reduce data transmission volume, thus reducing transmission delays. However, the limited computational capacity and energy of UAVs pose significant challenges to the efficiency of UAV-assisted MSAR systems. To address these problems, in this paper, we investigate a multi-UAV assisted MSAR system consisting of multiple Surveillance UAVs (S-UAVs) and a Relay UAV (R-UAV). Then, we formulate a joint optimization problem to minimize the maximum total latency among all S-UAVs via jointly making the computing offloading decisions, R-UAV deployment, and the association between a S-UAV and rescue targets while ensuring that all targets are monitored by S-UAVs. Since the formulated optimization problem is typically hard to solve due to its non-convexity, we propose an effective iterative algorithm by breaking it into three sub-problems. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

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.

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.