Aug 2026· Journal of Marine Science and Engineering· 0 citations· 104 references
TL;DR
It is argued that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance.
Abstract
Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collision risk, route efficiency, COLREGs compliance, vessel dynamics, and environmental uncertainty. Here we review the field through a unified framework based on planning scope, decision basis, and deployment requirements. We examine search- and sampling-based, geometric and rule-based, optimization-based, learning-driven, and hybrid methods, with particular emphasis on deep reinforcement learning for discrete decisions, continuous maneuvering, multi-vessel interaction, and safety-oriented control. Representative studies are compared across objective and reward design, state representation, exploration and policy optimization, rule integration, disturbance modeling, simulation platforms, and operational validation. The synthesis identifies persistent barriers, including ambiguous rule formalization, partial observability, strategic coupling among vessels, inconsistent benchmarks, limited cross-scenario generalization, and insufficient full-scale validation. We further discuss priority directions in explicit safety constraints, digital twins, transfer and meta-learning, world models, scalable multi-agent coordination, and large-model-assisted mission reasoning. We argue that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance.
UAV swarms are essential for emergency response, logistics, reconnaissance, and environmental monitoring, yet achieving safe and scalable path planning under dynamic conditions and complex constraints remains challenging. Unlike existing surveys that categorize algorithms by theoretical foundations, this paper systematically reviews UAV swarm path planning through the lens of spatial, temporal, and task-level consistency constraints. We classify recent advances into classical path search, intelligent optimization, and deep reinforcement learning, emphasizing how each addresses geometric continuity, behavioral coordination, and full-chain perception–decision–planning consistency under multi-constraint coupling. We further identify critical limitations in scalability, dynamic adaptability, and heterogeneous swarm cooperation, and outline future directions, including distributed control, multi-source perception fusion, cross‑platform collaboration, and robust autonomous decision-making. This review provides a unique, application‑centric taxonomy based on consensus constraints, offering actionable insights for developing consistency-aware UAV swarm path planning technologies.
Ya-Na Lu, Lian-Peng Li, Hui Zhao et al.· IAES International Journal o...· 0 citations
This paper presents an observation-only autonomy framework for Unmanned Underwater Vehicles (UUVs) navigation in dynamic underwater environments that integrates persistent occupancy mapping, global clearance-aware planning, and risk-aware local control. The proposed pipeline constructs occupancy maps solely from onboard sonar and depth image observations, adapts a clearance-constrained global planner (GP) to provide long-horizon structure, and integrates a reinforcement learning (RL) policy to handle short-range tracking and reactive avoidance. To further support decision-making under partial observability, the system learns a compact latent state representation from onboard sensor data, encoding environmental structure, obstacle dynamics, and uncertainty. Behavior tree (BT) distillation with staged supervision is introduced to improve safety and training stability, while an uncertainty-calibrated distillation mechanism reweights teacher guidance using online latent-model uncertainty, emphasizing uncertain regimes during learning, with time-to-collision (TTC) and clearance cues remaining explicit in planning and local policy features. To demonstrate the efficacy of the framework, a reproducible multi-seed evaluation protocol is established in high-fidelity GPU-accelerated simulation using NVIDIA Isaac Sim, and performance is benchmarked against BT-only and standard RL baselines. The results obtained demonstrate improved robustness and safety under dynamic conditions, thus providing a general pipeline with a unified hybrid planning learning architecture and a reproducible methodology for robust UUV autonomy under partial observability.
A dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions and constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks is proposed.
Changqi Yang, Hongjie Hu, Yi Ai· Drones· 0 citations
This paper addresses autonomous intervention with an underwater vehicle--manipulator system (UVMS) in confined, cluttered, and partially known environments, where poor maneuverability, narrow passages, and uncertain execution may cause the robot to enter unrecoverable regions. We propose MANTA, a three-layer hierarchical planning-and-control framework that couples passage accessibility, manipulation feasibility, and closed-loop execution. The first layer performs global connectivity reasoning in a conservative reduced base space to extract traversable corridor candidates toward the task region. The second layer refines each candidate corridor by jointly optimizing the continuous base motion and arm trajectory, producing a collision-free base--arm trajectory. The third layer learns a reach-and-hold base policy using Gaussian-process model-based reinforcement learning (MBRL) through MC-PILCO, enabling trajectory tracking and station keeping at the planned manipulation state. During execution, the framework monitors map updates and can trigger recovery and route repair when the active passage becomes infeasible. MANTA is evaluated in confined UVMS planning and closed-loop tracking experiments. Across 120 matched planning queries, it achieves higher task success than full-state sampling-based baselines while producing larger clearance margins and lower arm motion. The learned MC-PILCO policy further reduces position and yaw tracking errors on both training and unseen tube-like references. These results show MANTA as a structured and data-efficient framework for safe autonomous underwater intervention in caves, tubes, and cluttered subsea structures.
Mohamed Abdelwahab, Ruggero Carli, Damiano Varagnolo et al.· 0 citations
Efficient 3-D path planning for autonomous underwater vehicles (AUVs) in dynamic submarine environments presents a significant challenge due to complex seabed terrain, ocean currents, and obstacles. In view of the adaptability and generalization limitations of traditional methods, this article proposes the reward-adaptive prioritized experience replay (RAPER) mechanism to dynamically adjust the experience sampling priorities by evaluating the temporal-difference errors and the weights of critical reward events, including success rate, goal proximity, route length, obstacle avoidance, and ocean current utilization. Then, a deep reinforcement learning framework, the DSAC-T-RAPER algorithm, is established for underactuated AUVs by combining the RAPER mechanism and distributional soft actor–critic with three refinements (DSAC-T) algorithm. Meanwhile, the finite-step evolution and boundedness of the adaptive event weight are theoretically analyzed to guarantee the reliability and stability of the proposed algorithm. The 3-D simulation environment is constructed by considering complicated submarine seafloor topography, real ocean current data from the Copernicus Marine Environment Monitoring Service, and randomly distributed obstacles. Simulations under different scenarios demonstrate that, compared with some other algorithms, DSAC-T-RAPER achieves higher success rates and better performances.
Xin Cheng, Hai Jin, Yun Chen et al.· IEEE Systems Journal· 0 citations
To address the challenge that single algorithms struggle to balance global exploration and local obstacle avoidance, and are prone to falling into local optima in complex environments, this paper proposes a Strategic Hierarchical Path Planning (SHPP) framework. This framework decouples the 3D navigation task into three synergistic layers. The top layer employs a reinforcement learning network equipped with a Credit Alignment Mechanism (CAM) to provide macroscopic guidance, eliminating credit assignment pollution and escaping local minima. The middle layer introduces a Dual-Guided Particle Swarm Optimization (DG-PSO) algorithm to map discrete commands into continuous smooth trajectories. The bottom layer executes physical collision avoidance and tracking based on the Artificial Potential Field (APF) method. Simulations indicate that the system can establish a stable policy in approximately 625 episodes, achieving an average reward of 95.6. Furthermore, benefiting from the hierarchical architecture's smooth optimization in continuous space, the average flight path length is 138.3 meters, a reduction of approximately 19% compared to traditional discrete decision-making models. These quantitative results fully validate the superior performance of the proposed architecture in complex 3D environments.
Fei Wang, Jun-Yong Shi, Zhao-Kun Chen et al.· International Conference on...· 0 citations
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