Path Planning for UAV Based on Improved PRM

Liu, Weimin; Wang, Lei; Zou, Awei; Cai, Jingcao; Huang, He; Tan, Tielong

doi:10.3390/en15197267

Cited by 17 publications

(8 citation statements)

References 33 publications

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“…Random sampling algorithms are a category of sampling-based path-planning methods, such as rapidly exploring random trees (RRTs) [20] and probabilistic road maps (PRMs) [21]. These algorithms have worked well in practice for USVs and have shown the advantage of probabilistic completeness.…”

Section: Global Path-planning Methodsmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

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Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.

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Section: Global Path-planning Methodsmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Discretize the space and provide a more realistic view of how the UAV travels along its path to ensure a collision-free trajectory. Li W. et al ( 2022 ) proposed an improved probabilistic road map (IPRM) algorithm to solve the energy consumption problem of multi-UAVs path planning. The mathematical model and energy consumption model are established by simulating the real terrain environment.…”

Section: Related Workmentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

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Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

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“…Guo et al [4] introduced the Flight Cost-based Rapidly exploring Random Tree star (FC-RRT*) algorithm, which integrates a cost function to generate optimal paths, prioritizing safety, path length, and flight constraints efficiently in complex environments. Li et al [5] proposed the Improved Probabilistic Roadmap (IPRM) algorithm to tackle energy consumption challenges in multi-UAV path planning, achieving smoother and shorter paths with sampling of third-order B-spline curves for angle rotation. Li et al [6] developed a 3D UAV path-planning model combining an enhanced A-star algorithm with an improved R5DOS intersection model, reducing computational complexity and calculation time in three-dimensional spaces.…”

Section: Introductionmentioning

confidence: 99%

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Liu,

Lu,

Yang

et al. 2024

WEVJ

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In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

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Path Planning for UAV Based on Improved PRM

Cited by 17 publications

References 33 publications

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

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