Aug 2026· Journal of Marine Science and Engineering· Vol 14, pp. 1537· 0 citations· 36 references
Abstract
Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support.
The rapid scaling of advanced air mobility (AAM) makes corridor-based structured airspace a promising infrastructure for high-density unmanned aerial vehicle (UAV) traffic. Formation flight can improve corridor capacity by suppressing shockwave propagation, but rigid formations become inefficient or unsafe during ramp branching, merging, and congestion. To address this problem, this paper proposes a task-driven diverge-merge control framework for UAV formations in structured airspace. At the beginning, a corridor-ramp branching structured airspace model is established to characterize the traffic dynamics and spatial constraints. Building upon this, a fast task-driven clustering mechanism integrates spatial connectivity, flight intent, and aerial task interactions to enable real-time diverge and merge for ramp branching and traffic reshaping. To make the diverge-merge reconfigurations executable at the media access control (MAC) layer of the formation, a cluster-aware distributed time division multiple access (CAD-TDMA) protocol is further designed. It protects intra-cluster control synchronization while conservatively reusing low-risk inter-cluster slots. Simulation results show that the proposed diverge-merge algorithm maintains near-zero geometrical misclassification under severe physical overlapping and congestion. With the formation diverge-merge traces, CAD-TDMA achieves the best delay--loss--throughput tradeoff over fixed TDMA and WiFi MAC. It shows that the proposed formation control framework can jointly support real-time formation reconfiguration and reliable communication in corridor-ramp structured airspace.
Kai Xiong, Xingyu Wu, Baolong Zhang et al.· arXiv.org· 0 citations
Air refueling extends aircraft range and endurance, but its operational value hinges on where the refueling airspace is placed and how tanker missions are sequenced. This paper addresses the joint optimization of refueling airspace planning and tanker scheduling, in which each receiver selects a refueling point from a continuous feasible interval along a fixed route. The upper level determines refueling point locations (continuous variables), while the lower level schedules multiple heterogeneous tankers (discrete combinatorial variables); the two levels are tightly coupled through spatiotemporal constraints and fuel propagation. We propose a bottleneck-driven decoupled update (BDDU) strategy built on the Whale Optimization Algorithm (WOA). BDDU extracts bottleneck states from lower-level scheduling feedback and applies per-dimension step-size control to damp the coupling amplification effect inherent in bi-level optimization. Across three scenarios of varying coupling intensities and scales, BDDU-WOA raises the feasibility rate from 50% (WOA baseline) to 90% (+40 percentage points; p<0.05, Fisher’s exact test). The gain stems from a bottleneck-aware, dimension-wise step-size control mechanism with an adaptive, parameter-free classification threshold and only two tunable parameters, adding roughly 10% computational overhead. The method is intended for pre-mission planning of large-scale air refueling operations.
A service-driven regional partitioning scheme is proposed to support traffic-aware UAV communication, and an adaptive handshaking mechanism is introduced to improve cooperative sensing accuracy by mitigating residual inter-region phase errors with controlled synchronization overhead.
Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments.
Rakan Armoush, Shidrokh Goudarzi, Muhammad Nadeem Khan et al.· Italian National Conference...· 0 citations
Man Overboard (MOB) incidents require rapid rescue operations because delayed response significantly reduces the probability of victim survival. Conventional life buoys provide flotation assistance but cannot autonomously approach the victim, resulting in rescue operations that rely heavily on manual intervention by the ship's crew. This study aims to design and evaluate Varuna Buoy, an autonomous maritime rescue vehicle employing the Point-to-Point Navigation method to improve rescue response effectiveness. The proposed system integrates an ESP32 microcontroller, Neo-6M GPS receiver, HW127 digital compass, BTS7960 motor driver, modified bilge pump thrusters, and LoRa wireless communication module. Navigation is performed by combining Haversine distance estimation, azimuth bearing calculation, and differential thrust control to continuously minimize heading error while navigating toward predetermined target coordinates. The system was evaluated through ten autonomous navigation experiments conducted under two different initial heading scenarios at Ketintang Lake, Universitas Negeri Surabaya. Experimental results showed that the proposed system successfully navigated toward the target with final distances ranging from 1.27 m to 2.62 m, achieving a 70% navigation success rate based on a predefined stopping threshold of 2 m. Furthermore, the LoRa communication system achieved an average latency of 1.06 s, enabling reliable near real-time telemetry. These findings demonstrate that the proposed autonomous navigation system provides a practical and effective solution for maritime rescue applications and offers a promising foundation for future autonomous rescue vehicle development.
H. Eyrlangga· Indonesian Journal of Engine...· 0 citations
We evaluate the potential of equipping commercial ships with unmanned aerial vehicles (UAVs) that can be utilized as maritime search and rescue (SAR) resources and compare this to a system with UAVs dispatched from land-based stations. By leveraging data for Sweden on historical SAR incidents, as well as historical ship positions, a simulation-based geographic analysis is performed to estimate UAV response times from ships and land-based stations. These times are then compared with the historical response times for the SAR vessel that reached the incident first. Weather factors like wind, temperature, and precipitation, as well as different UAV characteristics are considered in the estimations. The results indicate that the average response time to SAR incidents can be reduced between 7 and 16 min, depending on UAV characteristics and SAR system design. Having fixed-wing UAVs dispatched from land-based stations is likely the best option from a response time reduction perspective, but it may induce higher socioeconomic costs than having multi-rotor UAVs on commercial ships. However, further studies are needed to estimate the cost-benefit ratio.
Anna-Maria Grönbäck, T. Granberg, Niki Matinrad· Safety Science· 0 citations
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