A real-time system for detection and avoidance of dynamic obstacles for micro UAVs

Molchanov, Andrii; Кортунов, В.И.; Mozolyuk, Stas

doi:10.1109/msnmc.2016.7783105

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…The first level is a rough plan and the second level is a fine plan. Then according to the above algorithm flow, the detection of dynamic obstacles can be found in time and obstacles avoidance can be performed [9].…”

Section: System Overviewmentioning

confidence: 99%

Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Bi¹,

Xu²,

Zhang³

2019

dtetr

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Considering the matters of accuracy, real-time, task adaptability and dynamic re-planning for the route planning of unmanned aerial vehicle. When the unmanned aerial vehicle is planning its route on a largescale map, it needs to ensure better real-time performance. If new obstacles are encountered during flight, the route planning algorithm needs to be able to carry out dynamic planning to re-plan the route to avoid the new obstacles. This paper presented an optimized route planning of unmanned aerial vehicle algorithm, which mainly through the management of number of search nodes and stratification strategy while still maintaining a safe route. We also presented a dynamic route re-planning function when encountering new obstacles by updating the map. The algorithm has better real-time performance and robustness. It has great reference value for the practical application of unmanned aerial vehicle.

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Section: System Overviewmentioning

confidence: 99%

Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Bi¹,

Xu²,

Zhang³

2019

dtetr

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“…In 2013, ETH Zurich studied an optical flow sensor called PX4Flow, which is widely used in UAVs to calculate the speed information of UAVs [3] . In 2016, Andrii et al [4] at Kharkiv National Aerospace University in Ukraine developed a small UAV obstacle detection system based on the optical flow method. In 2019, Tien et al [5] from the University of Minnesota proposed a parameter adaptive optical flow algorithm based on the characteristics of optical flow data, which improved the performance of the optical flow algorithm in calculating the horizontal velocity of the drone.…”

Section: Introductionmentioning

confidence: 99%

Flight state recognition for UAV optical flow velocity measurement

Yang

Liu

2023

J. Phys.: Conf. Ser.

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For the visual navigation and positioning of drones, the optical flow algorithm is used to calculate the flight speed of the drone, the calculation errors of its rotation state, and the vertical take-off and landing state. Based on the characteristics of its optical flow data, this paper compares various classification calculation methods. It uses the median KNN curve of the optical flow vector to simplify its feature quantity. Finally, it realizes the motion of the UAV with the optical flow characteristics based on the SVM algorithm. State recognition experiments have proved that this method can effectively identify the motion state of the UAV, and the calculation efficiency is high.

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Research on the Algorithm of Online Route Re-planning for UAV Formation

2020

J. Phys.: Conf. Ser.

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In order to solve the real-time and robust problem of online flight path re-planning when UAV encounters new threat sources in the course of navigation, an online flight route re-planning algorithm based on sparse A* algorithm is proposed. Based on the threat model of UAV, the online route re-planning strategy is analyzed. When the UAV detects a new threat in the course of navigation, according to the current position of the UAV formation and the position of the threat source, a route that can safely bypass the threat source is quickly re-planned. In order to meet the requirements of real-time and robustness, the node expansion and update strategy of the improved sparse A* algorithm and the hierarchical planning combined with the size step size are adopted. The simulation system of UAV formation navigation is established to verify the process of online route re-planning. The number of expansion nodes and convergence time of algorithm before and after the improvement are compared, and the track accuracy of fine planning based on rough planning is compared. The experimental results show that the improved algorithm can speed up the search speed and meet the real-time requirements of online route re-planning.

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A real-time system for detection and avoidance of dynamic obstacles for micro UAVs

Cited by 4 publications

References 4 publications

Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Flight state recognition for UAV optical flow velocity measurement

Research on the Algorithm of Online Route Re-planning for UAV Formation

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