Feature selection-based decision model for UAV path planning on rough terrains

Ali, Hamza; Xiong, Gang; Haider, Muhammad Husnain; Tamir, Tariku Sinshaw; Dong, Xisong; Shen, Zhongyao

doi:10.1016/j.eswa.2023.120713

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…We construct the repulsive potential field function based on the distance between the UGV and the obstacle, and the direction of the repulsive force vector obtained according to Equation (8) points to the UGV. The repulsive potential field U rep can be expressed as:…”

Section: Modified Potential Field Function Of Apf 421 Traditional Apf...mentioning

confidence: 99%

“…However, heuristic algorithms, as a primary approach to path planning, exhibit significant limitations in addressing the diversity and complexity of practical application environments. These traditional heuristic algorithms struggle to meet the requirements of more complex path planning scenarios due to inherent limitations [7][8][9]. For instance, Shengmin Zhao et al [7] proposed an innovative full-coverage path planning (CCPP) method, effectively addressing the dynamic tracking problem and solving multi-obstacle challenges in complex environments.…”

Section: Introductionmentioning

confidence: 99%

“…This method significantly improves the coverage of the planned path, achieving a high level of 98% through simulation experiments. Similarly, Hub Ali et al [8] focused on the UAV path planning and obstacle avoidance problems in a 3D terrain, proposing a trajectory planning technique capable of implementing global and local path planning for UAVs under static and dynamic constraints. This method demonstrates superior performance in handling complex terrain situations, significantly outperforming other 3D UAV path planning techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The motivation for this work is to address the specific limitations and gaps in existing path planning methods. Despite significant advancements made by various researchers [7][8][9][10][11][12], a comprehensive solution that efficiently and effectively tackles the challenges of dynamic and complex environments remains elusive. This paper introduces the BA*-MAPF algorithm, designed to surmount the limitations of existing path planning algorithms by offering a more adaptable, efficient, and reliable solution for UGVs navigating complex environments.…”

Section: Introductionmentioning

confidence: 99%

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A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.

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Section: Modified Potential Field Function Of Apf 421 Traditional Apf...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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“…The HAPF-ACO algorithm fully considers multiple UAV constraint types, and constructs target attraction and threat repulsion zones for environment cognition through HAPF, focusing on multi-UAV execution efficiency and obstacle avoidance performance [36]. The FSDM algorithm proposes a new feature selection-based decision-making model to improve multi-UAV recognition of the simulated environment, while the superior search capability of the A* algorithm is used to improve the efficiency of multi-UAV cooperative mission assignment [37]. The different model parameters and algorithm performance are shown in Table V.…”

Section: Experiments 3: Comparison Of the Performance Of Wafc-rras An...mentioning

confidence: 99%

Multi-UAV Cooperative Mission Assignment Based on Hybrid WAFC-RRAS Algorithm

Huang,

Hu,

Yang

et al. 2023

IEEE Access

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Multi-UAV cooperative mission assignment is an important research direction in the field of UAV research. In the planning process, the assignment conflict between UAVs and mission points and the solution efficiency are the key difficulties in multi-UAV cooperative mission assignment. Based on the Weighted Approximate Flight Cost (WAFC) method and the Reduced Redundant Assignment Scheme (RRAS) algorithm, a multi-UAV cooperative mission problem considering different assignment models is proposed. The contributions of this study are: firstly, the approximate flight cost matrix is constructed based on the vertical distance between the UAV and the mission point, and the matrix is applied to calculate the initial flight distance of the UAV, which fully takes into account the effects of mountain type and radar threat on the mission assignment. Secondly, the auxiliary flight cost method and the spatial mapping matrix are constructed, which maps the discrete approximation of the flight cost to the continuous space and solves the UAV's local conflict problem in the assignment process. Finally, an adaptive selection mutation strategy based on iterative individual fitness values is proposed to reduce the redundancy of candidate assignment schemes and improve the efficiency of the planning system. The simulation results verify that the algorithm has high cooperative capability, good robustness and fast solution speed when dealing with cooperative multi-UAV mission assignment planning.INDEX TERMS Vertical cut method, Approximate flight cost method, Mapping strategies, Assignment conflicts, Multi-UAV cooperative mission assignment.

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Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model

Liu,

Shao,

et al. 2024

Applied Soft Computing

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Feature selection-based decision model for UAV path planning on rough terrains

Cited by 7 publications

References 35 publications

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Multi-UAV Cooperative Mission Assignment Based on Hybrid WAFC-RRAS Algorithm

Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model

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