Realization of distributed formation flying using a group of autonomous quadcopters and application to visual performance show

Park, Myoung-Chul; Kang, Sung-Mo; Lee, Jae-Gyeong; Ko, Gwihan; Oh, Koog-Hwan; Ahn, Hyo‐Sung; Choi, Young-Cheol; Son, Ji-Hwan

doi:10.1109/itec-ap.2016.7513074

Cited by 10 publications

(5 citation statements)

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“…Regarding the computational aspects of swarm problems, references like [43,44,45], have presented their work towards a trajectory generation for a swarm application and the associated computational time. Moreover, the outdoor swarm implementations currently seen in the literature implement trajectories that are generated o✏ine [46,47,44,48]. With respect to the implementation of a generated trajectory, a common practice is to pass a trajectory to an autopilot which tracks the trajectory using a low-level flight controller [49].…”

Section: Real-time Trajectory Generation For Uavsmentioning

confidence: 99%

“…LITERATURE REVIEW communication using hardware currently available in the market. The reference [48] presented a study on communication method adopted for control of a swarm of quad-rotor for light shows. Similarly, in [55] Preiss et al presented a study on control of a swarm of quad-rotor UAVs in a laboratory environment.…”

Section: An Extension To Quad-rotor Swarm Problemsmentioning

confidence: 99%

“…Authors in [56] studied a distributed auction assignment method for control for a large swarm of agents. In addition, references [56,48] have studied an approach for a swarm trajectory generation in which trajectory is generated for an agent at a time in a swarm. In [41], the authors have used the simplified model of quad-rotor for trajectory generation.…”

Section: An Extension To Quad-rotor Swarm Problemsmentioning

confidence: 99%

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Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

Adhikari¹

2021

Preprint

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<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

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Section: Real-time Trajectory Generation For Uavsmentioning

confidence: 99%

Section: An Extension To Quad-rotor Swarm Problemsmentioning

confidence: 99%

Section: An Extension To Quad-rotor Swarm Problemsmentioning

confidence: 99%

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Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

Adhikari¹

2021

Preprint

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“…In the case of indoor swarming flight, motion capture or images can be used for position estimation. With the development of various sensors, various teams attempted to demonstrate swarm flight outdoors [ 8 , 9 , 10 ]. Later, Intel presented the first successful demonstration with 100 drones, in collaboration with Ars Electronica [ 10 ], and later presented a drone show involving more than 1000 drones during the 2018 Pyeongchang Winter Olympics, which was published in the Guinness Book of World Records.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm

Moon

Lee

et al. 2021

Sensors

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Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.

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“…Há trabalhos que utilizam voos em formação com aeronaves do tipo quadricóptero, como em (MASHOOD et al, 2015) no qual foram realizados estudos de voo cooperativo em formação para rastreamento de trajetórias. Em (PARK et al, 2016) foi realizado um estudo de voos em formação para apresentações de shows aéreos. Outros trabalhos também exploram quadricópteros como ferramentas.…”

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Controle baseado em observação de distúrbio de um quadricóptero sujeito a incertezas paramétricas e distúrbio de vento

Paiva¹

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Nesta dissertação é apresentado um estudo experimental da aplicação de diferentes controladores para o rastreamento de trajetória de um veículo aéreo não tripulado (VANT) de asas rotativas, do tipo quadricóptero, sujeito a incertezas paramétricas e distúrbio externo de vento com a utilização de uma fonte de distúrbio montada experimentalmente. Uma técnica de atenuação de distúrbio externo (DOBC-Disturbance Observer-Based Control ou Controle Baseado em Observação de Distúrbio) é usada e avaliada com dois observadores distintos, sendo um observador de distúrbio padrão e o filtro de Kalman aplicado a um sistema em espaço de estados aumentado. Foram realizados experimentos considerando distúrbios externos e incertezas paramétricas. A análise aqui descrita, baseia-se em um modelo dinâmico simplificado, o qual foi identificado via método dos mínimos quadrados. Os resultados experimentais mostram o desempenho de cada controlador aplicado e os índices de melhoria quando se utiliza a técnica DOBC. Alternativas de tratamento do distúrbio externo para realização em trabalhos futuros também são sugeridas.

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Realization of distributed formation flying using a group of autonomous quadcopters and application to visual performance show

Cited by 10 publications

References 13 publications

Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles

Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm

Controle baseado em observação de distúrbio de um quadricóptero sujeito a incertezas paramétricas e distúrbio de vento

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