A Time-Saving Path Planning Scheme for Autonomous Underwater Vehicles With Complex Underwater Conditions

Yang, Jiachen; Huo, Jiaming; Meng, Xiaofeng; He, Jingyi; Li, Zhengjian; Song, Houbing

doi:10.1109/jiot.2022.3205685

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…However, the variability of the gravity field significantly affects the performance of GAINS, making the selection of suitable navigation areas crucial [8,9]. In this regard, researchers have explored several quantitative characteristics such as variance, roughness, slope, coefficient of variation, fractal dimension, and their combinations to determine the efficiency of using gravity fields for navigation [10][11][12]. The navigation map is then divided into two categories : informative (suitable for positioning) and noninformative (not suitable for positioning) based on empirical thresholds [13].…”

Section: Autonomous Underwater Vehicles (Auv) Usually Use Inertial Na...mentioning

confidence: 99%

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Wang¹

2023

Preprint

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Gravity-aided inertial navigation system (GAINS) is an important development in autonomous underwater vehicle (AUV) navigation. An effective path planning algorithm plays an important role in the performance of navigation in long-term underwater missions. By combining the gravity information obtained at each position with the error information from the INS, the posterior Cram\'er-Rao bound (PCRB) of GAINS is derived in this paper. The PCRB is the estimated lower bound of position variance for navigation along the planned trajectory. And the sum of PCRB is used as the minimum cost from the initial state to the current state in the state space, and the position error prediction variance of inertial navigation system (INS) is used as the minimum estimated cost of the path from the current state to the goal state in the A* algorithm. Thus, a path planning method with optimal navigation accuracy is proposed. According to simulation results, traveling along the path planed by the proposed method can rapidly improve the positioning accuracy while consuming just slightly more distance. Even when measuring noise changes, the planned path can still maintain optimal positioning accuracy, and high positioning accuracy is possible for any trajectory located within a certain range of the planned path.

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Section: Autonomous Underwater Vehicles (Auv) Usually Use Inertial Na...mentioning

confidence: 99%

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Wang¹

2023

Preprint

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“…This improved path safety but did not consider the actual energy loss of the ship. In response to complex ocean current environments, an underwater path-planning method based on near-policy optimization was proposed by constructing a deep reinforcement network as a decision-making control system (UP4O), which integrated features of obstacles with current state information [21]. It was demonstrated that, under complex three-dimensional ocean currents, limited prior knowledge, and local information, it could find a time-saving collision-free path that narrowed the gap between AUV theoretical research and actual marine applications.…”

Section: Introductionmentioning

confidence: 99%

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

Chen,

Feng,

Bao

et al. 2024

JMSE

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Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current influences. The algorithm adeptly combines strategies for dynamic obstacle circumvention with an eight-directional current resistance approach, ensuring locally optimal paths that minimize the impact of currents on navigation. Additionally, advanced artificial bee colony algorithms were used during the research process to enhance the method and improve the smoothness of the generated path. Simulation results have verified the superiority of the algorithm in improving the quality of USV path planning. Compared with traditional bee colony algorithms, the improved algorithm increased the length of the optimization path by 8%, shortened the optimization time by 50%, and achieved almost 100% avoidance of dynamic obstacles.

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“…Fang et al [ 28 ] controlled the attitude of an AUV during navigation using the DDPG algorithm and solved the control fault problem at the critical value of the yaw angle. Yang et al [ 29 ] proposed a path-planning algorithm based on near-end strategy optimization; the proposed algorithm combines a deep reinforcement learning network with the features of local obstacles and selects the optimal strategy according to the environmental information. The results show that the paths generated by this algorithm are time-saving and collision-free in complex underwater environments.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Xing

Wang

Ding

et al. 2023

Sensors

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With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.

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A Time-Saving Path Planning Scheme for Autonomous Underwater Vehicles With Complex Underwater Conditions

Cited by 17 publications

References 31 publications

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

Path Planning Method for Underwater Gravity-Aided Inertial Navigation Based on PCRB

An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

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