Vision-based UAV collision avoidance with 2D dynamic safety envelope

Lyu, Yang; Zhao, Chunhui; Zhang, Yizhai; Hu, Jinwen

doi:10.1109/maes.2016.150155

Cited by 37 publications

(24 citation statements)

References 28 publications

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“…Regarding the line fit this will give a horizontal line with t C/y = ∞ which should be detected upon fitting the line (no intersection with time axis). On the other hand (16) shows that this case it is very easy to obtain CP A ′ y/y from the time average of theȳ S ′ y values.…”

Section: Vertical Formulae For Ttcpa and Cpa Estimationmentioning

confidence: 93%

“…However, in case of small UAVs the size, weight and power consumption of the onboard S&A system should be minimal. Monocular vision-based solutions can be cost and weight effective therefore especially good for small UAVs [7], [13], [16], [17], [20], [21] [22], [27]. These systems basically measure the position (bearing) and size of intruder aircraft (A/C) camera image without range and intruder size information.…”

mentioning

confidence: 99%

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Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid

Bauer

Hiba

Bokor

et al. 2018

J Intell Robot Syst

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The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach, the horizontal relative distance and its direction and the vertical relative distance also. The distances are relative to the intruder aircraft horizontal and vertical sizes. The overall estimated relative distance is the closest between the two aircraft in three dimension. So finally, every important information can be extracted to be used in a collision decision. The applicability of the developed method is presented in software-in-the-loop simulation test runs. Several intruder size and speed values are considered together with trajectories covering the whole three dimensional space. The horizontal intruder flight directions relative to the own aircraft cover Systems and Control Laboratory, MTA SZTAKI, H-1111 Budapest, Kende utca 13-17.

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Section: Vertical Formulae For Ttcpa and Cpa Estimationmentioning

confidence: 93%

mentioning

confidence: 99%

Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid

Bauer

Hiba

Bokor

et al. 2018

J Intell Robot Syst

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“…e is always a continuous function when β ± satisfies the Equation (22). Therefore, the controller (v m , ω m ) to avoid a collision is obtained by the Theorem 2. where ω avoid = v m sin e+ke D t cos β ± , k is a positive value.…”

Section: Collision Avoidance Controller Designmentioning

confidence: 99%

“…From Remark 2 and Definition 6, the general conditions to activate the collision avoidance control are given by Equations (22) and (23).…”

Section: Remarkmentioning

confidence: 99%

“…Let us assume that the vehicle moves from a start position S(x S , y S ) to a given point T(x T , y T ) and tracks a reference path. In the movement process, let C(x C , y C ) be the position at which both values of α and D t satisfy Equations (22) and (23), meaning that the collision avoidance algorithm is activated. Let N(x N , y N ) denote a position at which the collision avoidance is completed, meaning that values of α and D t do not satisfy conditions of Equations (22) and (23).…”

Section: Movement Strategymentioning

confidence: 99%

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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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Formation control of unmanned aerial vehicle swarms: A comprehensive review

Ouyang

Cong

et al. 2022

Asian Journal of Control

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The unmanned aerial vehicle formation plays a crucial role in numerous applications, such as reconnaissance, agricultural plant protection, and electric power inspection. This paper provides a comprehensive review and analysis of the unmanned aerial vehicle swarm communication networks and formation control strategies. First, the most commonly used unmanned aerial vehicles are introduced and compared. Next, the entire process of the formation task, from the formation assignment to the formation transformation, is detailed described. At last, the widely adopted communication networks are analyzed, and the existing formation control strategies of the UAV swarm are compared, which shows that the distributed formation control is superior to the centralized method and is the future development trend.

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Vision-based UAV collision avoidance with 2D dynamic safety envelope

Cited by 37 publications

References 28 publications

Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid

Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid

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

Formation control of unmanned aerial vehicle swarms: A comprehensive review

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