Path Planning for Autonomous Underwater Vehicle Based on Artificial Potential Field and Modified RRT

Jia, Zhu; Zhao, Shili; Zhao, Ran

doi:10.1109/icccr49711.2021.9349402

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…The artificial potential field proposed by Khatib [5,6] method is a method applied in the field of robot obstacle avoidance where the combined force of gravitational force and repulsive force is calculated as the traction force to make the AUV move. Zhu et al [7] and Cheng et al [8] realized path planning under ocean currents and obstacles by combining the artificial potential field with the heuristic method. The artificial potential field algorithm has a well-defined structure and strong obstacle avoidance capabilities, but it also has drawbacks such as local minima.…”

Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors.

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Section: Related Workmentioning

confidence: 99%

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Lyu,

Sun,

Wang

et al. 2023

JMSE

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“…Combined with the actual situation, the steering angle boundary value is set for the AUV. Only the inertia weight coefficient of the search decision point whose required steering angle exceeds the boundary is not 0 and follows the law that the greater the excess of steering angle over the boundary, the greater the coefficient [19]. The equation of the inertia weight coefficient is defined as:…”

Section: Search Task Benefitmentioning

confidence: 99%

Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment

Chen

et al. 2022

JMSE

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Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach.

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“…The algorithm is based on the idea of creating a virtual potential field around obstacles and guiding the robot toward the goal by minimizing the potential energy. The artificial potential field algorithm has been examined by several researchers for various applications such as mobile robots, wheelchairs (Matsuura et al, 2022), underwater vehicles (Cheng et al, 2015;Zhu et al, 2021), humanoid robots (Zhong et al, 2015), walking robots (Igarashi and Kakikura, 2004), planetary rovers(Sancho-Pradel and Saaj, 2014), autonomous sailboats (Plumet et al, 2013), and biofish (Iswanto et al, 2019). The artificial potential field algorithm has some advantages such as it is simple to implement and computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Hybrid Path Planning Algorithm Based on Apf and a-Star

Abdel-Rahman,

Zahran,

Elnaghi

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of robots had increased widely in various fields, such as air transport systems, search and rescue, and agriculture, necessitating the need for path planning and obstacle avoidance systems to ensure safe and autonomous navigation toward the intended goal. Many Path-planning techniques are used to guide the mobile robot toward its goal with an optimized path and time. Among these techniques, the Artificial Potential Field (APF) algorithm stands out as one of the effective approaches, capable of operating in both static and dynamic environments to achieve optimal path planning. The APF is built by fusing two forces that attract the robot toward a goal location and repulsive forces that repel the robot away from the obstacle. Despite its effectiveness, the APF algorithm faces a major challenge, which is falling into a local-minimum issue. This paper presents a hybrid approach that combines the modified APF algorithm global optimization capabilities with the A-Star path-planning technique's real-time adaptability to overcome traditional APF local minimum issues. The transition back and forth between the two algorithms where carried out by a manager that can determine the adequate algorithm to be used instantaneously. Several experiments are presented to demonstrate the hybrid algorithm's effectiveness in various environments. The results show that the enhanced APF reach an optimal path to goal 50% faster compared to A-star, and managed to get out of local minimum compared to traditional APF and find a path shorter than A-star.

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Path Planning for Autonomous Underwater Vehicle Based on Artificial Potential Field and Modified RRT

Cited by 16 publications

References 15 publications

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning

Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment

Enhanced Hybrid Path Planning Algorithm Based on Apf and a-Star

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