Practical Parallel of Autonomous Unmanned Aerial Vehicle by Mission Planner

Suparnunt, Chairat; Boonvongsobhon, Chon; Leelalerthpat, Prachaya; Hematulin, Warunyu; Kamsing, Patcharin

doi:10.1109/igarss46834.2022.9884577

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The server environment operates on Windows 10, with hardware specifications including an Intel ® Core™ i7-8700 CPU, 16 GB of RAM, and a GeForce RTX 2080 TI GPU. In terms of software, Python serves as the primary programming language, while DroneKit-Python [40] is employed for UAV flight control, and the UAV platform is Mission Planner [41,42].…”

Section: Software and Hardwarementioning

confidence: 99%

An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems

Chang,

Yen,

Chang

et al. 2024

Machines

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Because the average number of missing people in our country is more than 20,000 per year, determining how to efficiently locate missing people is important. The traditional method of finding missing people involves deploying fixed cameras in some hotspots to capture images and using humans to identify targets from these images. However, in this approach, high costs are incurred in deploying sufficient cameras in order to avoid blind spots, and a great deal of time and human effort is wasted in identifying possible targets. Further, most AI-based search systems focus on how to improve the human body recognition model, without considering how to speed up the search in order to shorten the search time and improve search efficiency, which is the aim of this study. Hence, by exploiting the high-mobility characteristics of unmanned aerial vehicles (UAVs), this study proposes an integrated YOLOv5 and hierarchical human-weight-first (HWF) path planning framework to serve as an efficient UAV searching system, which works by dividing the whole searching process into two levels. At level one, a searching UAV is dispatched to a higher altitude to capture images, covering the whole search area. Then, the well-known artificial intelligence model YOLOv5 is used to identify all persons in the captured images and compute corresponding weighted scores for each block in the search area, according to the values of the identified human bodies, clothing types, and clothing colors. At level two, the UAV lowers its altitude to sequentially capture images for each block, in descending order according to its weighted score at level one, and it uses the YOLOv5 recognition model repeatedly until the search target is found. Two improved search algorithms, HWFR-S and HWFR-D, which incorporate the concept of the convenient visit threshold and weight difference, respectively, are further proposed to resolve the issue of the lengthy and redundant flight paths of HWF. The simulation results suggest that the HWF, HWFR-S, and HWFR-D search algorithms proposed in this study not only effectively reduce the length of a UAV’s search path and the number of search blocks but also decrease the search time required for a UAV to locate the search target, with a much higher search accuracy than the two traditional search algorithms. Moreover, this integrated YOLOv5 and HWF framework is implemented and tested in a real scenario to demonstrate its capability in enhancing the efficiency of a search and rescue operation.

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Section: Software and Hardwarementioning

confidence: 99%

An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems

Chang,

Yen,

Chang

et al. 2024

Machines

View full text Add to dashboard Cite

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“…One type of control command comprises UAV flight control commands, such as speed, acceleration, heading angle, etc., which can be sent via a remote controller or ground station; another is the flight path target point position information, which is generally sent by the ground station to make the UAV fly to the target position [ 10 ]. At present, the common UAV ground station system developed by the mission planner can realize the configuration and simple flight control of the UAV [ 11 ]. DJI has developed DJI GS PRO ground station based on iPad, which can realize UAV fixed-path planning and automatic return based on a remote sensing map [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas

Huagang

et al. 2023

Sensors

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Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.

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Practical Parallel of Autonomous Unmanned Aerial Vehicle by Mission Planner

Cited by 2 publications

References 7 publications

An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems

An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas

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