A dynamically hybrid path planning for unmanned surface vehicles based on non-uniform Theta* and improved dynamic windows approach

Han, Sen; Wang, Lei; Wang, Yiting; He, Huacheng

doi:10.1016/j.oceaneng.2022.111655

Cited by 34 publications

(20 citation statements)

References 32 publications

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“…In order to avoid the USV getting stuck in local low points following the evasion of dynamic obstacles, Ref. [46] proposed an improved dynamic window approach (IDWA) that adopts a dynamic selection process for the parent node as a local target. This is a departure from the DWA method, which adheres to a fixed sub-target along the global path.…”

Section: Dynamic Window Approach Algorithmmentioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

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Section: Dynamic Window Approach Algorithmmentioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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“…The core innovation of Theta* lies in its elimination of angle constraints that depend on grid shapes, resulting in improved path smoothness and optimality. Specifically, in Theta*, the parent node of the current node can be any obstacle-free node in the map, removing the constraint of proximity found in the A* algorithm [5,6].…”

Section: Algorithmmentioning

confidence: 99%

Informed-TRRT*: An improved sampling-based path planning algorithm

Shi,

2024

DCDS-S

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Path planning involves finding the optimal path in a known environment, allowing robots or vehicles to reach the destination as quickly and safely as possible from the starting point. In this domain, the RRT* (Rapidlyexploring Random Tree Star) algorithm has emerged as an advanced path planning approach, in handling challenges in complex environments. However, due to the slow convergence rate of RRT*, and the significant uncertainty in the generated paths, achieving stability in generating optimal paths within a fixed number of iterations poses challenges for the RRT* algorithm. This paper develops a novel algorithm, Informed-TRRT*, which combines the Theta* algorithm with the Informed-RRT* algorithm. The algorithm proposed in this paper rapidly obtains a short initial solution using the Theta* algorithm, narrows the sampling region based on this initial solution to improve sampling efficiency, and incorporates pruning algorithms to further enhance path optimality. Through comparative simulation experiments in environments of increasing complexity, the proposed algorithm generates paths with lengths optimized by 1.23% and 3.56% compared to the Theta* algorithm and Informed-RRT* algorithm, respectively, in environments with gradually increasing complexity; 19.53% and 8.76%; 6.45% and 9.07%.

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“…To address USVs' inability to efficiently avoid dynamic obstacles, NT* was introduced to establish a mapping relationship and add minimum path costs to the objective function. The IDWA algorithm was proposed to achieve real-time obstacle avoidance and improve dynamic collision avoidance capabilities [13], but it is difficult to directly establish a universal standard for parameter selection due to the limitations of single scenarios and strong coupling between weight parameters. Due to the complexity of the interactive environment, dynamic obstacle avoidance path planning poses a significant challenge to the mobility of intelligent agents.…”

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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A dynamically hybrid path planning for unmanned surface vehicles based on non-uniform Theta* and improved dynamic windows approach

Cited by 34 publications

References 32 publications

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Informed-TRRT*: An improved sampling-based path planning algorithm

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

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