Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

Al-Kaff, Abdulla; García, Fernando; Martín, David; Escalera, Arturo de la; Armingol, José María

doi:10.3390/s17051061

Cited by 92 publications

(59 citation statements)

References 35 publications

(42 reference statements)

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“…Vários trabalhos tem destacado o desafio de realizar a detecção e desvio de obstáculos em veículos aéreos não tripulados (ARMY, 2010;YEARBOOK, 2011;AUTHORITY, 2012;DOD, 2013;YANG;BRUGGEMANN, 2012;AL-KAFF et al, 2017). Alguns deles afirmam a necessidade de possuir um sistema de detecção e desvio de obstáculos para permitir a inserção desses veículos no espaço aéreo, seja ele segregado ou não-segregado (AUTHORITY, 2012;DOD, 2013).…”

Section: Detecção E Desvio De Obstáculos Em Vants -Estado Da Arteunclassified

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: Detecção E Desvio De Obstáculos Em Vants -Estado Da Arteunclassified

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

Chiaramonte¹

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“…In [23], a method to detect changes in image patches is proposed in order to understand variations in the environment and obstacle sizes. In [24], the authors propose an approach for obstacle detection and avoidance. It uses a monocular camera on UAV and motion control measurements to imitate the human behavior in detecting objects in the environment.…”

Section: Related Workmentioning

confidence: 99%

“…As in [14], we take into consideration the percentage of the area in the cell to be covered, in order to increase the coverage rate. The work in [24] drops the whole cells that contain obstacles. Hence, if the obstacle occupies a small portion of a cell, the whole cell won't be covered, thus the area coverage rate decreases.…”

Section: Related Workmentioning

confidence: 99%

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EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Ghaddar

Merei

2020

Future Internet

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The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

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“…The derivative of α is computed from Equations (17) and (18) as follows: α can be re-written as follows:…”

Section: Collision Avoidance Controller Designmentioning

confidence: 99%

Nonlinear Control for Autonomous Trajectory Tracking While Considering Collision Avoidance of UAVs Based on Geometric Relations

2019

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Trajectory tracking with collision avoidance for a multicopter is solved based on geometrical relations. In this paper, a new method is proposed for a multicopter to move from the start position to a desired destination and track a pre-planned trajectory, while avoiding collisions with obstacles. The controller consists of two parts: First, a tracking control is introduced based on the errors between the relative position of the multicopter and the reference path. Second, once the obstacles with a high possibility of collision are detected, a boundary sphere/cylinder of the obstacle is generated by the dimensions of the vehicle and the obstacles, so as to define the safety and risk areas. Afterwards, from the relation between the vehicle’s motion direction, and the tangential lines from the vehicle’s current position to the sphere/cylinder of the obstacle, a collision detection angle is computed to decide the fastest direction to take in order to avoid a collision. The obstacle/collision avoidance control is activated locally when an object is close, and null if the vehicle moves away from the obstacles. The velocity control law and the guidance law are obtained from the Lyapunov stability. In addition, a proportional controller is used at the end of vehicle’s journey to ensure the vehicle stops at the target position. A numerical simulation in different scenarios was performed to prove the effectiveness of the proposed algorithm.

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Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

Cited by 92 publications

References 35 publications

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

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

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Nonlinear Control for Autonomous Trajectory Tracking While Considering Collision Avoidance of UAVs Based on Geometric Relations

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