Multi-drone Control with Autonomous Mission Support

Paula, N.; Areias, Bruno; Reis, André B.; Sargento, Susana

doi:10.1109/percomw.2019.8730844

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…The platform can abstract control details completely, allowing inexperienced users to plan, execute, and monitor complex missions with one or more UAVs. Furthermore, devices such as parachutes and sirens could be added to UAVs and activated when abnormal behavior is detected [41]. Yao et al used an optimal mission assignment method and multiple quadcopters to minimize the time required for spraying [42].…”

Section: Discussionmentioning

confidence: 99%

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Lee

Shin

Thomasson

et al. 2022

Sensors

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Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.

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Section: Discussionmentioning

confidence: 99%

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Lee

Shin

Thomasson

et al. 2022

Sensors

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“…Numerous approaches have been made in this area to deal with cooperative patrolling [25] and tracking operations [23,24], cooperative surveillance [15], and cooperative rescue missions [16]. In this context, a number of difficulties have been addressed, including path overlap avoidance [28], scheduling trajectories [17], and obstacle avoidance [26,27]. Numerous works have emphasized the use of artificial intelligence systems, including Multi-Agent Systems, to cooperatively generate harmony within a swarm of drones and carry out the expected activities in accordance with particular criteria [29,30] like time, energy limits, quality of service, etc.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Systems using UAV's for Surveillance in the Disaster Management

Shandilya,

Kuamr

2023

Preprint

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Rescue and recovery operations are critical in disaster management. For the speedy rescue process in order to save the lives, proper surveillance of the disaster affected area is essential. In this regard, surveillance using Unmanned Aerial Vehicles (UAV's) are predominately helpful in such circumstances for speedy surveillance. In this context, for faster surveillance, multiple UAV's are deployed in the disaster affected region. In this paper, the multiple UAV's are considered as multi-agent systems and explored the algorithms such as UAV back-and-forth algorithm, UAV FA algorithm and UAV random algorithm. The simulation results are carried out and evaluated the algorithms with respect to various parameters such as, residual battery in the drones, number of targets, average hit time and energy consumption. Based on the simulation results, the requirements and the need of the multi-agents for overall efficient surveillance for disaster management can be analysed based on the gathered information.

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“…Image acquisition in UAVs is done by path planning [8][9][10], and the image is acquired by a high-resolution camera i.e., First Person View (FPV) camera with the stabilized gimbal. Path planning is nothing, but the aerial movement of UAV is planned in a full-featured firmware mission planner [3,[11][12][13][14][15]. There are so many numbers of firm wares (soft wares) that are present for controlling Planes, Copters, and Rovers.…”

Section: Autonomous Moving Agricultural Uav For Image Acquisitionmentioning

confidence: 99%

“…Waypoints can also be changed by considering the criterion of track distance by the following Eq. (14).…”

Section: Head For Next Waypointmentioning

confidence: 99%

Diseased Area Recognition and Pesticide Spraying in Farming Lands by Multicopters and Image Processing System

Pittu¹,

Gorantla²

2020

JESA

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United Nations indexes that the world's population will increase drastically, it may increase by 20% and it will be around 970 crores by 2050. However this scenario requires 20% more food to serve this much population, but farming land is decreasing due to pollution, industrialization, and globalization. Cultivation these days is more difficult and increasing the labor cost and pesticides. Enough labor is not available, so labor charges are also increasing which reflects in expenditure for cultivation. In regular spraying system pesticides are sprayed in the whole field. This will increase pesticide costs and pollution. In this proposed system image acquiring Multicopter will move in a pre-defined path in the field is given in a Mission planner firmware. UAV while moving along the pre-defined path captures high-quality RGB pictures in the field and those captured images are tagged with a Global Positioning System (GPS). These pictures are processed by the image processing system using MATLAB software at the ground station and actual disease recognition from the database frame is done. Disease recognition is done along with the tag of the specific diseased area in the field. These tags can help the crew to identify the diseased area spot. This diseased area or plant GPS tag information is used for the creation of a path for pesticide spraying Multicopters and will spray the pesticide in a diseased area or a plant in the field. This will lead to the optimal usage of pesticides.

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Multi-drone Control with Autonomous Mission Support

Cited by 9 publications

References 26 publications

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Multi-Agent Systems using UAV's for Surveillance in the Disaster Management

Diseased Area Recognition and Pesticide Spraying in Farming Lands by Multicopters and Image Processing System

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