Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty
Practical applications of microaerial vehicle face significant challenges including imprecise localization, limited on-board energy, and motion uncertainty. This paper focuses on the latter two issues. The core of proposed energy-optimal path planning algorithm is an energy consumption model derivin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9994680 |
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| _version_ | 1832549661850730496 |
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| author | Yamin Li Bowen Sun Ping Xia Yang Yang |
| author_facet | Yamin Li Bowen Sun Ping Xia Yang Yang |
| author_sort | Yamin Li |
| collection | DOAJ |
| description | Practical applications of microaerial vehicle face significant challenges including imprecise localization, limited on-board energy, and motion uncertainty. This paper focuses on the latter two issues. The core of proposed energy-optimal path planning algorithm is an energy consumption model deriving from real measurements of a specific quadrotor and utilizing a 2D Gaussian distribution function to simulate the uncertainty of random drift. Based on these two models, we formulate the optimal path traversing the 3D map with minimum energy consumption using a heuristic ant colony optimization. Multiple sets of contrast experiments demonstrate the effectiveness and efficiency of the proposed algorithm. |
| format | Article |
| id | doaj-art-76fcb4cfd1c8473f99a33f00106ede4b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-76fcb4cfd1c8473f99a33f00106ede4b2025-02-03T06:10:45ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99946809994680Energy-Optimal 3D Path Planning for MAV with Motion UncertaintyYamin Li0Bowen Sun1Ping Xia2Yang Yang3School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaHubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, ChinaSchool of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaPractical applications of microaerial vehicle face significant challenges including imprecise localization, limited on-board energy, and motion uncertainty. This paper focuses on the latter two issues. The core of proposed energy-optimal path planning algorithm is an energy consumption model deriving from real measurements of a specific quadrotor and utilizing a 2D Gaussian distribution function to simulate the uncertainty of random drift. Based on these two models, we formulate the optimal path traversing the 3D map with minimum energy consumption using a heuristic ant colony optimization. Multiple sets of contrast experiments demonstrate the effectiveness and efficiency of the proposed algorithm.http://dx.doi.org/10.1155/2021/9994680 |
| spellingShingle | Yamin Li Bowen Sun Ping Xia Yang Yang Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty Complexity |
| title | Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty |
| title_full | Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty |
| title_fullStr | Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty |
| title_full_unstemmed | Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty |
| title_short | Energy-Optimal 3D Path Planning for MAV with Motion Uncertainty |
| title_sort | energy optimal 3d path planning for mav with motion uncertainty |
| url | http://dx.doi.org/10.1155/2021/9994680 |
| work_keys_str_mv | AT yaminli energyoptimal3dpathplanningformavwithmotionuncertainty AT bowensun energyoptimal3dpathplanningformavwithmotionuncertainty AT pingxia energyoptimal3dpathplanningformavwithmotionuncertainty AT yangyang energyoptimal3dpathplanningformavwithmotionuncertainty |