A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

Lie, Fidelis Adhika Pradipta; Go, Tiauw Hiong

doi:10.1007/s12555-010-0519-7

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Nas abordagens cooperativas, existe alguma forma de comunicação entre os elementos envolvidos de forma a coordenar suas posições para evitar colisões entre si. Quando associadas a veículos aéreos, estas abordagens são utilizadas geralmente em estudos relacionados ao voo em formação (LIE; GO, 2010;BAYHA;GRÜNEIS;SCHÄTZ, 2012;GONCALVES et al, 2013;JESUS et al, 2013).…”

Section: Optimal Reciprocal Collision Avoidance -Orcaunclassified

Detecção e desvio de obstáculos para veículos aéreos não tripulados usando visão monocular

Chiaramonte¹

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Veículos autônomos são importantes para a execução de missões dos mais variados tipos, reduzindo riscos aos seres humanos e executando as missões de uma maneira mais eficiente. Neste contexto existem os veículos aéreos não tripulados que são cada vez mais utilizados em missões de vigilância, reconhecimento, resgate, entre outras. Uma das características destes veículos é realizar as missões de maneira autônoma, sem a intervenção de operadores humanos. Desta forma, é necessário que existam formas de detectar aproximações perigosas com outras aeronaves e objetos que possam causar risco de colisão e, consequentemente a perda de ativos de alto valor ou até mesmo vidas humanas e, posteriormente realizar o desvio necessário. Neste cenário foi proposto o MOSAIC, um sistema de detecção e desvio de obstáculos utilizando visão monocular para veículos aéreos de pequeno porte. Para isto, foi desenvolvido um método de estimativa da posição tridimensional dos obstáculos a partir de imagens monoculares e propostas melhorias em algoritmos de detecção. A validação do sistema foi obtida por meio de experimentos simulados e reais sobre cada módulo e os resultados obtidos foram promissores, apresentando um erro de apenas 9,75% em ambientes sem restrições e distâncias de até 20 metros. Com isto, os resultados se mostram melhores que os demais algoritmos encontrados no estado da arte em que o erro é menor que 10% apenas em ambientes controlados e distâncias de até 5 metros.Palavras-chave: MOSAIC, Detecção e desvio de obstáculos, Visão monocular, Posição tridimensional, ORCA.ABSTRACT CHIARAMONTE, R. B. Obstacle avoidance for UAVs using monocular vision. 2019. 136 p. Tese (Doutorado em Ciências -Autonomous vehicles can be used for different kinds of missions reducing risks to human life and being more efficient. In this context, unmanned aerial vehicles play an important role on surveillance, recognition and rescue missions, among others. Due to the mission nature, these vehicles need to perform actions without human intervention, which requires that dangerous approximations to others aerial vehicles or objects to be detected and properly avoided. This leads to the creation of MOSAIC, an obstacle avoidance system based on monocular vision designed to meet the requirements of miniature air vehicles. A novel approach to estimate obstacle three-dimensional position based on monocular vision was developed and some improvements in the detection algorithm were proposed. The system validation was obtained through simulated and real experiments in which each module could be validated. Promising results were obtained showing an error under 9.75% in unconstrained environments and distance up to 20 meters. This results were better than the algorithms and approaches described in the state of the art where errors are under 10% only on constrained environments and distance up to 5 meters.

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Section: Optimal Reciprocal Collision Avoidance -Orcaunclassified

Detecção e desvio de obstáculos para veículos aéreos não tripulados usando visão monocular

Chiaramonte¹

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“…In order to solve the multiple UAVs search problem, the literature [26] takes the method of distributed model predictive control that transforms the centralized multiple UAVs optimization decision problem on line to each UAV small scale distributed optimization problem and then adopts the algorithm which is based on particle swarm optimization and Nash optimization achieves iterative solution for each subsystem optimization problem. Based on optimal trajectory generator coupled with a modified sliding controller for tracking the trajectory and avoiding collisions, the literature [27] accomplishes a UAV formation reconfiguration control scheme with autonomous collision avoidance system for application in 3D space. When facing the uncertainties and obstacles, the literature [28] adopts the learning based model predictive control (LBMPC) to solve the formation reconfiguration problem for a group of cooperative UAVs forming a desired formation.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Zhou¹,

Kang²,

Dai³

et al. 2016

Mathematical Problems in Engineering

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For various threats in the enemy defense area, in order to achieve covert penetration and implement effective combat against enemy, the unmanned aerial vehicles formation needs to be reconfigured in the process of penetration; the mutual collision avoidance problems and communication constraint problems among the formation also need to be considered. By establishing the virtual-leader formation model, this paper puts forward distributed model predictive control and finite state machine formation manager. Combined with distributed cooperative strategy establishing the formation reconfiguration cost function, this paper proposes that adopting the revised quantum-behaved particle swarm algorithm solves the cost function, and it is compared with the result which is solved by particle swarm algorithm. Simulation result shows that this algorithm can control multiple UAVs formation autonomous reconfiguration effectively and achieve covert penetration safely.

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