FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Guo, Yicong; Liu, Xiaoxiong; Liu, Xuhang; Yang, Yue; Zhang, Weiguo

doi:10.3390/ijgi11020112

Cited by 27 publications

(14 citation statements)

References 44 publications

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“…Compared with ordinary UAVs, it reduces human resources, saves working time, and greatly reduces the difficulty of work. Moreover, compared with the new type of UAV, the ordinary UAV is larger in size, faster in power consumption, and higher in cost, and its production and use are not a small amount [19]. The emergence of new unmanned aerial vehicles not only reduces human and material resources but also greatly reduces expenditure [20].…”

Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

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With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.

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Section: Development Status Of Reinforcement Learning Uav In Various ...mentioning

confidence: 99%

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Gao

Liu

et al. 2023

Journal of Function Spaces

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“…Since the RRT algorithm is a probabilitybased search method, there is randomness as well as blindness in its search process. The above deficiencies will lead to lower search efficiency, as well as the possibility of getting into a local deadlock and failing to find a path [24]. Moreover, the paths obtained by the RRT algorithm do not have continuity, and trajectories need to be optimized [25].…”

Section: Rrt Algorithm Principlementioning

confidence: 99%

Path planning and trajectory optimization based on an improved RRT algorithm

Teng,

Wang

2024

Preprint

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This study presents an enhanced motion planning and trajectory optimization algorithm for Rapidly-exploring Random Trees (RRT), addressing the issues of excessive randomness, unnecessary path segments, and discontinuities in the trajectory that are evident in conventional RRT path planning algorithms. Initially, a dynamic sampling strategy was developed to minimize the search's randomness by managing the generation location of random points. Subsequently, the artificial potential field method was incorporated into the RRT algorithm to enable nodes to account for the influence of obstacles and target points during expansion; thus, mitigating the search's aimlessness. Redundant sections were then eliminated, and the path distance was reduced, all the while maintaining a safe distance from obstacles. Finally, by employing the minimum snap method in conjunction with flight corridors and time reallocation, the trajectory was rendered smooth and continuous, ensuring that physical quantities remained within acceptable limits without encountering obstacles. The comparative simulation results demonstrate that the proposed algorithm markedly decreases the number of nodes extended during the path search and reduces the search path length in comparison to the traditional RRT.

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“…Algoritma perencanaan jalur disebut bersifat asimtotik optimal jika dapat menjamin bahwa algoritma tersebut akan menghasilkan solusi optimal jika diberikan jumlah iterasi/waktu yang mencukupi [1,2]. Kriteria solusi optimal dapat didasarkan pada satu atau beberapa kondisi, seperti jarak terpendek, kenyamanan, risiko terendah, ataupun kebutuhan bahan bakar yang lebih sedikit [3].…”

Section: Pendahuluanunclassified

Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm

Pohan

2023

Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan

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Suatu algoritma perencanaan jalur bersifat asimptotik optimal jika dapat menjamin bahwa akan menghasilkan solusi optimal jika diberikan waktu yang memadai. Algoritma perencanaan jalur yang dapat memberikan solusi optimal sangat penting dalam banyak aplikasi robotik. Penelitian ini bertujuan untuk mengusulkan algoritma perencanaan jalur baru yang bersifat asimptotik optimal. Metode yang digunakan adalah dengan meningkatkan algoritma probabilistic road map (PRM) melalui tiga strategi. Strategi pertama adalah menggunakan teknik pembangkitan sampel berbasis informasi. Strategi kedua adalah area pencarian dimulai dari subset ellipsoid yang berukuran kecil dahulu. Strategi ketiga adalah melakukan perbaikan jalur menggunakan proses wrapping. Algoritma yang diusulkan ini dinamakan algoritma wrapping-based informed PRM (WIPRM). Selanjutnya, performansi algoritma WIPRM dibandingkan dengan algoritma PRM, informed rapidly-exploring random tree* (RRT*), dan informed PRM. Hasil pengujian menunjukkan bahwa algoritma WIPRM dapat membangun jalur yang optimal untuk semua scenario pengujian. Waktu komputasi yang dibutuhkan algoritma WIPRM untuk membangun jalur optimal lebih baik daripada algoritma informed RRT* dan informed PRM. Dengan demikian, algoritma WIPRM berpotensi untuk diimplementasikan dalam berbagai sistem robotik yang membutuhkan algoritma perencanaan jalur yang optimal seperti mobil otonom, unmanned aerial vehicles (UAV), maupun autonomous underwater vehicles (AUV).

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FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Cited by 27 publications

References 44 publications

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment

Path planning and trajectory optimization based on an improved RRT algorithm

Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm

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