UAV Path Planning Based on Improved A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>∗</mo></math>and DWA Algorithms

Bai, Xiong; Jiang, Haikun; Cui, Juncheng; Lu, Kuan; Chen, Pengyun; Zhang, Ming

doi:10.1155/2021/4511252

Cited by 47 publications

(20 citation statements)

References 23 publications

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“…Xu et al [13] used particle swarm optimization to optimize the parameters of artificial potential field method and combined with fuzzy rules to avoid obstacles, but their fuzzy rules tended to stay away from moving obstacles and could not flexibly deal with moving obstacles. Bai et al [14] proposed a path planning algorithm based on A * and DWA. The key points of the global path were selected as the subtarget points of the local path planning, and the global optimal path evaluation subfunction was constructed.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

2022

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Aiming at the problem of local path planning for unmanned surface vehicles, the traditional dynamic window algorithm (DWA) has the problems of uneven obstacle avoidance path and poor adaptability to complex environments. This paper proposes a local path planning method based on improved DWA. The evaluation item that replaces the direction angle difference with the target distance is used to avoid the path from being unsmooth due to vibration. And the fuzzy logic control algorithm is introduced to dynamically adjust the weight value of the trajectory evaluation function to complete the path optimization. The introduction of the A ∗ algorithm is designed to preplan the map environment and extract key turning points as subtarget points of the local obstacle avoidance algorithm to enhance the path optimization ability. The simulation results show that the improved DWA algorithm can shorten the planned path length by 23.4% and the path smoothness by 34.8%, enabling USV to find a reasonable, efficient, and smooth path in complex environments.

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Section: Introductionmentioning

confidence: 99%

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

2022

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“…e heuristic function of the traditional A * algorithm is shown in equation (3), which does not consider the influence of obstacle density on search efficiency. Since there are usually many obstacles between the starting point and the target point, the algorithm will occupy ample memory space and reduce the efficiency during the search process, and generate more redundant nodes [21]. When the number of obstacles in the environment space is small, the value of the heuristic function of A * is closer to the actual distance value, so the weight of the heuristic function can be increased appropriately to reduce the algorithm's search space and improve the search efficiency.…”

Section: Improving the Heuristic Function Of Jps-a * Algorithmmentioning

confidence: 99%

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Zhen

Yang

et al. 2022

Journal of Robotics

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To address the problems of the traditional A ∗ algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A ∗ algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A ∗ algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A ∗ and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.

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“…Based on the amount of information, there are global and local planning. Dijkstra [1][2][3], A* [4][5][6][7], and D* [8] algorithms are used globally. Locally, the dynamic window approach [9] and artificial potential field method [10][11][12] are used.…”

Section: Introductionmentioning

confidence: 99%

Research on fusing improved A* and adaptive DWA path planning algorithms

LIAO,

WANG,

QIN

2024

Preprint

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To satisfy the performance requirements of robot path planning, an algorithm combining the improved A* algorithm and the improved Dynamic Window Approach (DWA) is proposed, which results in shorter path lengths, improved search efficiency, and path smoothness. Aiming at the challenges of the traditional A* algorithm in complex scenarios, a new heuristic function based on Manhattan and diagonal is designed, and then weights are dynamically assigned to obtain the global shortest path and the least search time. Then, an improved search strategy based on 8-neighborhoods is proposed, which improves the search efficiency and reduces the time consumption of the traditional 8-neighborhood 8-direction search method by dynamically assigning the optimal search direction of the current node. On the other hand, the traditional DWA algorithm faces some challenges, such as the paths are not globally optimal, the path planning may fail or path length may increase, the azimuthal coefficient is rigid, and the algorithm is computationally intensive. For these problems, a keypoint densification strategy is proposed to modify the deflected paths, adaptively adjust the azimuth function coefficients, and limit the range of the obstacle distance function. Finally, the proposed improved A* algorithm and fusion algorithm are compared with the existing methods. The simulation results under the ROS system show that the improved A* algorithm can generate the shortest global path in complex environments, the average path length is reduced by 3.95%, and the average path searching time is shortened by 21.62%. For the fused algorithm, the average path length and the average runtime are reduced by 5.95% and 8.7% in the moving obstacles environment.

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UAV Path Planning Based on Improved A $*$ and DWA Algorithms

Cited by 47 publications

References 23 publications

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Research on fusing improved A* and adaptive DWA path planning algorithms

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UAV Path Planning Based on Improved A∗and DWA Algorithms

Cited by 47 publications

References 23 publications

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Research on fusing improved A* and adaptive DWA path planning algorithms

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UAV Path Planning Based on Improved A $*$ and DWA Algorithms