Local Path Planning for Unmanned Surface Vehicle based on the Improved DWA Algorithm

Tan, Zhikun; Wei, Naxin; Liu, Zhengfeng

doi:10.23919/ccc55666.2022.9901807

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…Nonetheless, the conventional application of this algorithm often results in a path that circumvents the outer edges of densely populated obstacle regions. This elongates the distance traveled and brings the path in close proximity to dynamic obstacles [45]. The dynamic window approach is a method for local route planning that relies on sampling velocities.…”

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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“…Additionally, the objective cost function fails when a "C"-shaped obstacle is encountered, resulting in no path [33] being found. Tan et al [34] introduced the concept of the obstacle search angle to the traditional DWA. When the static obstacle is not within a certain angle range of the agent's forward direction, the impact on the agent's subsequent navigation is not considered, which improves the adaptability of obstacle avoidance in different scenarios.…”

Section: Local Path Planningmentioning

confidence: 99%

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

Wang,

Zhang,

et al. 2023

Drones

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In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.

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“…Unmanned vessels are widely used and play an important role in shipping industry, scientific research and military fields [2,3] . In recent years, the design and research of unmanned transport vessel have been the top priority in the Marine traffic information engineering and control discipline, and the path planning of unmanned transport vessel has been a hot topic for researchers at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

“…The commonly used path planning algorithms of unmanned transport equipment include Dijkstra algorithm, greedy algorithm, ant colony algorithm, A* algorithm, genetic algorithm, etc. [3] , all of which have advantages and disadvantages. Therefore, in the research of unmanned vessel path planning, innovative traditional algorithms are usually adopted to solve practical problems [2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Path planning for ships avoiding movable obstacles based on improved greedy algorithm

Qi¹,

Chen²,

Bian³

2023

Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023)

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In order to improve the safety of Unmanned Surface Vessel (USV) sailing in the ocean with movable obstacles, a new method based on greedy algorithm, ant colony algorithm and grid method is proposed. By adding periodic repeated calculation of movable obstacles with known paths, the route is periodically replanned. The approximation method is used to solve the problem that the traditional greedy ant colony algorithm is difficult to avoid the movable obstacles in the optimal course design of unmanned surface vessel. The algorithm uses greedy algorithm to plan the optimal route and ant colony algorithm to get rid of the situation that greedy algorithm is easy to enter the local convergence state. By rationalizing the movable obstacles on the known path, the navigation water limit of unmanned surface vessel caused by movable obstacles is avoided. The simulation results show that the algorithm is practical and reasonable for optimal route design when USV avoids movable obstacles. The simulation results show that the algorithm has the disadvantage of long search time.

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Local Path Planning for Unmanned Surface Vehicle based on the Improved DWA Algorithm

Cited by 8 publications

References 11 publications

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

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

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

Path planning for ships avoiding movable obstacles based on improved greedy algorithm

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