An Improved Artificial Potential Field Method for Path Planning of Mobile Robot with Subgoal Adaptive Selection

Lin, Zenan; Yue, Ming; Xiang-min, WU; Tian, Haoyu

doi:10.1007/978-3-030-27526-6_19

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Zhou and his colleagues improved the APF method with a particle swarm algorithm to increase the path finding efficiency for tangent navigating robots [44]. Lin and his colleagues designed a subgoal algorithm for the APF such that the path planning of the unmanned vehicle can overcome the local minimum and track the most optimal path [45]. The decision tree was added to the APF to form the efficient path planning algorithm without local minimum and collisions for vehicles [46].…”

Section: Artificial Potential Field Methodsmentioning

confidence: 99%

“…(1) Decompose the surrounding area into nonoverlapping but connected cells (2) Address the optimal path between the origin and the destination cells without collisions Artificial Potential Field [41][42][43][44][45][46][47] (1) Predefine a virtual artificial potential field (2) Assume the destination provides the attractive force while obstacles generate repulsive force to the vehicle (3) Address the optimal path for the vehicle through the field descending route Intelligent Path Planning Algorithms [48][49][50][51][52][55][56][57][58][59][60][61][62][63][64] (GA, ACO, Fuzzy logic, NN, and RL)…”

Section: Logic Benefits Drawbacksmentioning

confidence: 99%

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Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

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The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

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Section: Artificial Potential Field Methodsmentioning

confidence: 99%

Section: Logic Benefits Drawbacksmentioning

confidence: 99%

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

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“…Due to the complexity and temporal variability of the marine environment, as well as the diversity of autonomous underwater vehicle (AUV) planning tasks [6,7], adaptive sampling with MOPs requires efficient path-planning technology to ensure the smooth completion of tasks. Algorithms currently applied in path planning include the Dijkstra [8], Bellman Ford [9], Floyd-Warshall [10], A-star [11], Dynamic Programming [12], Artificial Potential Field [13], and Linear Quadratic [14] algorithms. Heuristic algorithms have also been applied, including the Genetic [15], Ant Colony [16], and Particle Swarm Optimization [17] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Zhang,

Liu,

Zhou

2023

JMSE

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Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning strategy based on evolutionary deep reinforcement learning. The low sampling efficiency of the reinforcement learning algorithm is improved by evolutionary learning. The combination of these two components as a new algorithm has become a current research trend. We first combined the evolutionary algorithm with different reinforcement learning algorithms to verify the effectiveness of the combination of algorithms with different strategies. Experimental results indicate that the fusion of the two algorithms based on a maximum-entropy strategy is more effective for adaptive sampling using a 3D marine observation platform. Data assimilation experiments indicate that adaptive sampling data from a 3D mobile observation platform based on evolutionary deep reinforcement learning improves the accuracy of marine environment numerical prediction systems.

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“…The path planning problem of multi-rotor unmanned vehicles has been resolved through the combination of the APF and biogeography based method (Song et al, 2019). Lin and colleagues designed a subgoal algorithm to the APF such that the path planning of the unmanned vehicle can overcome the local minimum and track the optimal path (Lin et al, 2019). Decision tree has been added to the APF to form an efficient path planning algorithm without local minimum and avoiding collisions for the vehicles (Lin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

Zhu

Yang

2021

J. Navigation

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To eliminate the effect of ocean currents for optimal path planning for unmanned underwater vehicles (UUVs) in the underwater environment, an intelligent algorithm is designed and proposed in this paper. The algorithm consists of two parts: an artificial potential field-based algorithm that derives the shortest path and avoids collision accidents; and an adjusting function that eliminates the effect of ocean currents. The planning results of the intelligent algorithm are presented in detail, and compared with the conventional algorithm that does not consider the effect of currents. The effectiveness of the optimised path planning method given in this paper is proved.

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An Improved Artificial Potential Field Method for Path Planning of Mobile Robot with Subgoal Adaptive Selection

Cited by 5 publications

References 8 publications

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Adaptive Sampling Path Planning for a 3D Marine Observation Platform Based on Evolutionary Deep Reinforcement Learning

Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

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