Obstacle avoidance for micro quadrotor based on optical flow

Gao, Pinghai; Zhang, Daibing; Fang, Qiang; Jin, Shaogang

doi:10.1109/ccdc.2017.7979206

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Obstacle detection, defined as the process of detecting proximity to objects that could potentially impede motion and cause a collision, has been extensively studied in literature for UAVs, using both sensors [13], [14] and image processing techniques [15], [16], [20], [17]. In [13], 16 infrared sensors and 12 ultrasonic sensors were employed to provide 360°c overage for obstacle detection, while [21], [14], [15] leveraged optical flow sensors to estimate the distance to obstacles. Optical flow was supported by stereo vision techniques in [14] whereas [16] relied on a single monocular camera, and estimated the distance to obstacles by calculating the change in size of feature points of obstacles in consecutive frames.…”

Section: Related Workmentioning

confidence: 99%

FPV Video Adaptation for UAV Collision Avoidance

Singh

Lee

Tran

et al. 2021

IEEE Open J. Commun. Soc.

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First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

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Section: Related Workmentioning

confidence: 99%

FPV Video Adaptation for UAV Collision Avoidance

Singh

Lee

Tran

et al. 2021

IEEE Open J. Commun. Soc.

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“…The avoidance direction of the vehicle is determined based on angle α. From Equations (16) and 17, it is easy to see that: If 0 ≤ α < β + ⇔ ψ mo ≤ ψ m < ψ mo + β + , meaning that the orientation of velocity v m is in ∠NMO. The fastest avoidance direction is the left side of the obstacle.…”

Section: Statement Problemmentioning

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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“…However, they performed the experiments with the off-board computer (i.e., desktop PC) since the feature matching algorithm demands a heavy computational power. On the other hand, optical flow-based obstacle avoidance methods can be readily applicable to the lightweight on-board computer of the MAV as they require much less computational power compared to the feature-based algorithms [4,5,6,7,8,9,10,11,12,13,14]. Souhila et al implemented an obstacle avoidance strategy for the ground vehicle by changing a heading angle according to the amount of the optical flow difference between the left and right half planes of the image, called the balance strategy [4].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments

Cho

Kim

2019

Sensors

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Due to payload restrictions for micro aerial vehicles (MAVs), vision-based approaches have been widely studied with their light weight characteristics and cost effectiveness. In particular, optical flow-based obstacle avoidance has proven to be one of the most efficient methods in terms of obstacle avoidance capabilities and computational load; however, existing approaches do not consider 3-D complex environments. In addition, most approaches are unable to deal with situations where there are wall-like frontal obstacles. Although some algorithms consider wall-like frontal obstacles, they cause a jitter or unnecessary motion. To address these limitations, this paper proposes a vision-based obstacle avoidance algorithm for MAVs using the optical flow in 3-D textured environments. The image obtained from a monocular camera is first split into two horizontal and vertical half planes. The desired heading direction and climb rate are then determined by comparing the sum of optical flows between half planes horizontally and vertically, respectively, for obstacle avoidance in 3-D environments. Besides, the proposed approach is capable of avoiding wall-like frontal obstacles by considering the divergence of the optical flow at the focus of expansion and navigating to the goal position using a sigmoid weighting function. The performance of the proposed algorithm was validated through numerical simulations and indoor flight experiments in various situations.

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Obstacle avoidance for micro quadrotor based on optical flow

Cited by 9 publications

References 12 publications

FPV Video Adaptation for UAV Collision Avoidance

FPV Video Adaptation for UAV Collision Avoidance

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

Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments

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