Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis

Cheng, Chunlei; Zhu, Daqi; Sun, Bing; Chu, Zhenzhong; Nie, Jianduo; Zhang, Sheng

doi:10.1109/ccece.2015.7129363

Cited by 47 publications

(30 citation statements)

References 14 publications

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“…Its main task is to output the deterministic action value t for the input state s t . The update of online policy network parameters is shown in Equation (19).…”

Section: Actormentioning

confidence: 99%

“…Chao [18] adopted optimization theory, combined artificial potential field with obstacle constraint, and transformed path planning problem into solving constraint and semi-constraint problems. Cheng et al [19] used velocity vector synthesis algorithm to enable the combined velocity of ocean current velocity and AUV velocity point to the target, thereby minimizing resource consumption. In Ferrari et al [20], aiming at the problem of collaborative planning of multi-AUV to avoid multi-detection platform network, the detection platform was considered a virtual obstacle, and the planning result could determine the minimum exposure probability and the non-collision path by modifying the fitness function.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Sun

Luo

Ran

et al. 2021

JMSE

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This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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“…Its main task is to output the deterministic action value t for the input state s t . The update of online policy network parameters is shown in Equation (19).…”

Section: Actormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Sun

Luo

Ran

et al. 2021

JMSE

View full text Add to dashboard Cite

show abstract

“…Thus, the traditional artificial potential field method cannot satisfy the requirements and must be improved. Regarding dynamic obstacle avoidance, Cheng et al [135] added a velocity synthesis algorithm for AUVs to artificially avoid obstacles in the artificial potential field algorithm, simulation results show that the speed is increased by 15.1% while avoiding obstacles accurately. In [136], a dynamic formation model for Multi-AUV with a complex underwater environment was developed.…”

Section: ) Artificial Potential Field (Apf)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

138

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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“…However, FM is limited in the nonlinear computational efficiency. APF can incorporate various linear terms, inlcuding energy, obstacles, travel time and ocean currents [10], [15], [16], but, it has the drawbacks of producing locally optimal solutions. RRT is also a widely used method to solve the underwater path planning [11], [17].…”

Section: Introductionmentioning

confidence: 99%

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

Liu

Gao

et al. 2019

IEEE Access

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AUV docking requires the platform to have both wide range cruising and accurate operating abilities, to against challenges of ocean currents, obstacles, and constraints. This paper proposed an evolutionary-based method, for the purpose of docking path optimization. First, the ocean environment and constraints are analyzed and modeled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and quantum behavior are introduced in particle swarm optimization (PSO), to achieve global time-optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.INDEX TERMS AUV docking, path optimization, quantum behaved evolutionary algorithm.

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Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis

Cited by 47 publications

References 14 publications

A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

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