Efficient Perception and Obstacle Avoidance Flight of UAVs in Dynamic Dense Environments

Safe flight in dynamic environments is crucial for the application of Unmanned Aerial Vehicles (UAVs), Existing methods lack precise classification criteria and overlook the limited field of view. Therefore, this paper introduces an efficient dynamic obstacle perception and avoidance method suitable for UAVs with limited vision in dense dynamic environments. It includes effectively distinguishing the velocities of obstacles to enhance point cloud segmentation and motion estimation. Moreover, we have designed efficient trajectory planning and active perception strategies considering the limited field of view. We have validated the superiority of the algorithm in both simulated and real-world experiments, confirming their effective environmental perception and obstacle avoidance capabilities in dynamic environments.

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Publication Details

Published
2024-12-10
DOI
https://doi.org/10.1109/robio64047.2024.10907502
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Efficient Perception and Obstacle Avoidance Flight of UAVs in Dynamic Dense Environments

Youshen Lin, Weiqi Gai, Jiachi Ji, Zhijun Meng et al.
Robotic Path Planning Algorithms
article

Efficient Perception and Obstacle Avoidance Flight of UAVs in Dynamic Dense Environments

Youshen Lin, Weiqi Gai, Jiachi Ji, Zhijun Meng, Zichen Wang
article en

Abstract

Safe flight in dynamic environments is crucial for the application of Unmanned Aerial Vehicles (UAVs), Existing methods lack precise classification criteria and overlook the limited field of view. Therefore, this paper introduces an efficient dynamic obstacle perception and avoidance method suitable for UAVs with limited vision in dense dynamic environments. It includes effectively distinguishing the velocities of obstacles to enhance point cloud segmentation and motion estimation. Moreover, we have designed efficient trajectory planning and active perception strategies considering the limited field of view. We have validated the superiority of the algorithm in both simulated and real-world experiments, confirming their effective environmental perception and obstacle avoidance capabilities in dynamic environments.

Beihang University (CN)
National Natural Science Foundation of China, Aeronautical Science Foundation of China
Life below water
Openalex Percentile: Top 72%
Robotic Path Planning Algorithms
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