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This paper presents a novel 3D path planning method for the underwater glider (UG) that incorporates adaptive segment strategy, motion parameters optimization, and an improved JADE algorithm. The method aims to generate an energy-efficient path by adapting to ocean currents and seabed topography and selecting favorable motion parameters. We establish an energy consumption model for a blended-wing-body UG, examining the influence of motion parameters and ocean currents on its performance. The proposed method encodes an energy-optimal gliding path through multiple path segments, each defined by a set of path points, pitch angles, and diving depths. The fitness function, based on energy consumption, guides the optimization process. To enhance the optimization, we present an improved JADE algorithm with multi-mutation strategies, which adaptively updates the mutation operation and mean crossover probability. Our method was assessed and compared with a classical UG path planning method on 6 test scenarios. Simulation results confirm that adaptive segments and motion parameter optimization contribute to better adaptation to ocean environments and reduced energy consumption. • A 3D path planning method for underwater gliders with adaptive segments and optimal motion parameters was proposed. • An energy consumption model for a blended wing body underwater glider was established. • An improved JADE algorithm with multi-mutation strategies was presented as an optimizer for UGAPP. • Significant improvements have been achieved in terms of energy savings for the underwater glider compared to a classical method. [ABSTRACT FROM AUTHOR]

Path planning is a key factor for the unmanned surface vehicle (USV) to achieve efficient navigation. In this paper, to solve the global path planning and obstacle avoidance problems for the USV, an improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed. Three main improvement parts are composed of the proposed algorithm. Firstly, the radial basis function (RBF) neural network is combined with the Q-Learning algorithm to approximate the action value function Q, which improves the convergence speed of the Q-Learning algorithm. Secondly, to ensure that the planned path conforms to the maneuvering characteristics of the USV, the heading angle, motion characteristics, ship length, and safety of the USV are taken into account by the proposed algorithm. Based on these factors, the action space and reward function are optimized, the state space is reconstructed, and the safety threshold is proposed. Finally, a third-order Bezier curve is used to smooth the initial path, so that the USV can maintain its heading stability during navigation. Based on simulation results, the proposed NSFQ algorithm outperforms the A* and RRT algorithms in terms of evaluation indicators such as heading angle, angular velocity, path length, sailing time, and path smoothness. • An improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed in this paper. • The heading angle, angular velocity, and other indicators of the USV are taken into account by this proposed algorithm. • A safety threshold is proposed to ensure the safety of the USV. • A third-order Bezier curve is used to smooth the initial path. [ABSTRACT FROM AUTHOR]

The increasing number of bridges in inland waterways has been continuously posing the potential for ship-bridge collision accidents. To reduce the probability of accidents in the inland bridge area and facilitate the safe passage of the autonomous ship, this paper proposes a path planning method with multiple vector fields, which comprehensively considers the characteristics of inland waters, hydrological characteristics, and meteorological factors. In this method, the navigational environmental factors affecting the path generation are divided into two parts, the static vector field, and the dynamic vector field, in the modeling part of the navigation environment. The static vector field is composed of the obstacle vector field and the channel vector field, and the dynamic vector field is constructed by the simulated wind field and currents field. The resulting synthetic vector field is used as input to the Anisotropic Fast Marching (AFM) algorithm to calculate the optimal path for the autonomous ship under influence of multiple factors. The complete algorithm has been tested and validated through three different scenarios and is evaluated according to four different evaluation criteria. The contribution of this work is to develop a path planning method that could incorporate external environmental influences and navigational risk into the path planning process. The research results of this paper can provide an important reference for the research on the path planning of autonomous ships in the inland bridge area. • The characteristic elements of inland bridge waters are considered in path planning. • The obstacle repulsion field, channel field and simulated wind field are generated by the traditional FM algorithm. • A multi-vector field mixing method is proposed to generate the optimal route with AFM method. [ABSTRACT FROM AUTHOR]

Recently, there has been growing interest in developing Autonomous Underwater Vehicle (AUV) to operate for longer mission durations as well as with higher levels of autonomy. This paper describes the current state of the art in methodologies to enable long range AUVs and provides a detailed literature review of some existing AUVs characterizing their operation endurance. Path planning is identified as the core and crucial components to improve AUV persistence. The aims of path optimization, path re-planning adapting to the dynamic environments, and cooperative path planning of multiple AUVs have received much attention from the research community. This paper presents a review of the main research works focusing on these three technology areas. The main objective of this paper is to present a comprehensive survey of shape and properties of the path and optimization techniques for path planning. These techniques and algorithms have been classified into different classes and their assumptions and drawbacks have been discussed. Finally, the paper discusses the AUV literature in general and highlights challenges that need to be addressed in developing AUVs with advanced autonomy and capable of operating for longer mission durations. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) have obtained increasing interests in recent decades. Because of the features of improved mission efficiency and decreased resource costs, applications of USVs can been seen in both civilian and naval areas. In order to efficiently and effectively achieve missions without any human intervention, a robust and intelligent navigation, guidance and control (NGC) system is vital for USV. This paper has therefore presented a novel NGC system designed for a USV named Springer . The system is developed by integrating multiple functional modules, which include a reliable navigation module that provides reliable position and heading information, a robust autopilot module enabling Springer tracking well the waypoints and an intelligent path planning module that is capable of generating feasible and practical waypoints. The path planning algorithm has been developed based upon the angle guidance fast marching square method, which is able to calculate the optimal path according to vehicle's motion constraints. The designed NGC system has been validated in both real field trials and computer based simulations proving that Springer USV is able to autonomously navigate in different maritime environments with the guidance of the NGC system. [ABSTRACT FROM AUTHOR]

Aiming at the path planning problem for searching static target by autonomous underwater vehicles (AUVs) in ocean environment, this paper proposes a hierarchical method based on the maximum cumulative detection reward or search efficiency. First, the mission sea region is described in the form of target probability map, and the subregions with high search value are extracted by the method of expectation-maximization (EM). For multiple AUVs, we propose the bidirectional negotiation with biased min-consensus (BN-BMC) algorithm to determine the allocation and prioritization of subregions to be searched. On this basis, we utilize an adaptive oval spiral coverage (AOSC) strategy to plan the coverage path within each subregion, and take the Dubins path satisfying the AUV kinematic constraint as the transition path between subregions. Finally, the simulation results validate the advantage of our method. • This paper proposes a hierarchical method to solve the AUV path planning problem for target search in ocean environment. • The bidirectional negotiation with biased min-consensus (BN-BMC) theory is proposed to allocate and prioritize subregions. • The adaptive oval spiral coverage (AOSC) strategy is used to achieve the full coverage of an oval-shape subregion. [ABSTRACT FROM AUTHOR]

The predictive uncertainty of ocean currents needs to be considered in the path planning of autonomous underwater vehicles (AUV) in oceanic environments. An interval optimization (IO) scheme is presented in this paper to plan time-optimal paths for AUVs operating in oceanic environments with dynamic and uncertain flow fields; interval currents are established to process the uncertainty of forecasted ocean flows, and the IO scheme is used to search for time-optimal paths in the interval current fields. The IO scheme is a two-layer framework: In the outer layer, quantum-behaved particle swarm optimization is used to generate and optimize candidate paths. In the inner layer, the interval response of the candidate path from the outer layer is calculated under interval current fields, and they are converted into a time-optimal fitness value in accordance with the possibility degree of interval numbers. The influence of predictive uncertainty is analyzed via simulation, and the robustness of the IO scheme is verified. • QPSO and interval optimization schemes are introduced for AUV path planning in uncertain, time-varying flow fields. • A two-layer framework algorithm is used to generate the time-optimal paths. • Vector analysis method is applied to compute the interval responses and reduce computational time. • Extensive simulation results to verify reliability of time-optimal paths to forecast errors. [ABSTRACT FROM AUTHOR]