Implementation of Matrix Drone Show Using Automatic Path Generator with DJI Tello Drones

Virbora, Ny; Sokoeun, Un; Meas, Saran; Sovicheyratana, Sous; Srun, Channareth; Saravuth, Srim

doi:10.1109/iceet56468.2022.10007090

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…The authors of [54] use Tello agents to demonstrate an automated swarm flight in a restricted flight space. A matrix formation control that uses Tello to display patterns was adopted in [55]. In [56], the DJI frame was used to build visionless sensing drones for obstacle avoidance and maze solving.…”

Section: Performance Testsmentioning

confidence: 99%

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Phadke,

Medrano

2024

Aerosp. Res. Commun.

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Resilient UAV (Unmanned Aerial Vehicle) swarm operations are a complex research topic where the dynamic environments in which they work significantly increase the chance of systemic failure due to disruptions. Most existing SAR (Search and Rescue) frameworks for UAV swarms are application-specific, focusing on rescuing external non-swarm agents, but if an agent in the swarm is lost, there is inadequate research to account for the resiliency of the UAV swarm itself. This study describes the design and deployment of a Swarm Specific SAR (SS-SAR) framework focused on UAV swarm agents. This framework functions as a resilient mechanism by locating and attempting to reconnect communications with lost UAV swarm agents. The developed framework was assessed over a series of performance tests and environments, both real-world hardware and simulation experiments. Experimental results showed successful recovery rates in the range of 40%–60% of all total flights conducted, indicating that UAV swarms can be made more resilient by including methods to recover distressed agents. Decision-based modular frameworks such as the one proposed here lay the groundwork for future development in attempts to consider the swarm agents in the search and rescue process.

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Section: Performance Testsmentioning

confidence: 99%

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Phadke,

Medrano

2024

Aerosp. Res. Commun.

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“…This trend has accelerated with advancements in control, networking, and AI technologies. Examples include multidrone entertainment such as drone shows [1,2], disinfection and pest control systems using multiple robots, goods movement within logistics centers [3], and military applications such as Manned-Unmanned Teaming (MUM-T) [4,5]. These innovations represent significant progress in the utilization of multi-robot systems across diverse fields.…”

Section: Introductionmentioning

confidence: 99%

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

Kwon,

Yun,

Kim

et al. 2024

Applied Sciences

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This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with minimized constraints. This paper introduces a coupled error that links the distance errors with the leading robot and the following robot through a coupling ratio. We propose a controller using the coupled error to achieve the control objectives of this paper. Unlike existing results that only use the information of the preceding robot, this algorithm couples the information of both the preceding robot and one’s follower. Using the proposed error-coupling-based formation control algorithm, multi-robot systems can quickly establish formations for collaboration, allowing tasks to commence swiftly and reducing deformations in formations due to speed variations. With stability analysis and simulation results for the practical application of the proposed algorithm, the approach has been verified to improve the speed of both the completion of the formation and overall system trajectory tracking, balancing trade-offs between them.

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“…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 ]. In addition to being directly selected by the operator, the location information of the path target point can also be analyzed and calculated by the intelligent algorithm.…”

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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Implementation of Matrix Drone Show Using Automatic Path Generator with DJI Tello Drones

Cited by 5 publications

References 16 publications

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Increasing Operational Resiliency of UAV Swarms: An Agent-Focused Search and Rescue Framework

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

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

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