Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment
This work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approa...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833615/ |
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| author | Abera Tullu Yunsang Cho Sangho Ko |
| author_facet | Abera Tullu Yunsang Cho Sangho Ko |
| author_sort | Abera Tullu |
| collection | DOAJ |
| description | This work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an obstacle by an ellipsoidal surface defined by its eigenvectors and eigenvalues. The eigenstructure specifies a region occupied by the obstacle so that the path planner re-routes the path whenever the UAV comes closer to this region. The proposed path planner is verified to bypass the challenges and limitations of computationally lightweight path planners such as artificial potential fields. The path planner is also compared with rapidly exploring random tree and shows better performance in optimal path and low computational time. The performance of the proposed path planner is validated in a simulated environment filled with obstacles of various sizes, shapes, and orientations. |
| format | Article |
| id | doaj-art-d3e1c02c709e4852a31247228dceb21c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d3e1c02c709e4852a31247228dceb21c2025-01-15T00:02:30ZengIEEEIEEE Access2169-35362025-01-01138077809110.1109/ACCESS.2025.352712310833615Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered EnvironmentAbera Tullu0https://orcid.org/0000-0002-2385-3167Yunsang Cho1https://orcid.org/0009-0003-6102-8779Sangho Ko2https://orcid.org/0000-0003-0481-7969Department of Smart Air Mobility, Korea Aerospace University, Goyang-si, Gyeonggi-do, Republic of KoreaDepartment of Smart Air Mobility, Korea Aerospace University, Goyang-si, Gyeonggi-do, Republic of KoreaDepartment of Smart Air Mobility, Korea Aerospace University, Goyang-si, Gyeonggi-do, Republic of KoreaThis work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an obstacle by an ellipsoidal surface defined by its eigenvectors and eigenvalues. The eigenstructure specifies a region occupied by the obstacle so that the path planner re-routes the path whenever the UAV comes closer to this region. The proposed path planner is verified to bypass the challenges and limitations of computationally lightweight path planners such as artificial potential fields. The path planner is also compared with rapidly exploring random tree and shows better performance in optimal path and low computational time. The performance of the proposed path planner is validated in a simulated environment filled with obstacles of various sizes, shapes, and orientations.https://ieeexplore.ieee.org/document/10833615/Expectation maximizationGaussian mixture modelobstacle avoidancepath planningunmanned aerial vehicles |
| spellingShingle | Abera Tullu Yunsang Cho Sangho Ko Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment IEEE Access Expectation maximization Gaussian mixture model obstacle avoidance path planning unmanned aerial vehicles |
| title | Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment |
| title_full | Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment |
| title_fullStr | Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment |
| title_full_unstemmed | Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment |
| title_short | Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment |
| title_sort | gaussian mixture model based vector approach to real time three dimensional path planning in cluttered environment |
| topic | Expectation maximization Gaussian mixture model obstacle avoidance path planning unmanned aerial vehicles |
| url | https://ieeexplore.ieee.org/document/10833615/ |
| work_keys_str_mv | AT aberatullu gaussianmixturemodelbasedvectorapproachtorealtimethreedimensionalpathplanninginclutteredenvironment AT yunsangcho gaussianmixturemodelbasedvectorapproachtorealtimethreedimensionalpathplanninginclutteredenvironment AT sanghoko gaussianmixturemodelbasedvectorapproachtorealtimethreedimensionalpathplanninginclutteredenvironment |