Collision Avoidance of Unmanned Aerial Vehicles In an Urban Environment

Choi, Daegyun; Kim, Dong-Hoon; Lee, Kyuman

doi:10.1109/naecon49338.2021.9696377

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Obstacle avoidance is a crucial aspect of drone operations in urban environments. Pioneering research was performed by Choi and others [15], but they focused only on building avoidance.…”

Section: Obstacle Avoidancementioning

confidence: 99%

Real‐time collision‐free landing path planning for drone deliveries in urban environments

Lee,

Cho,

Jung

2023

ETRI Journal

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This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the “landing angle control” method to allow the drone to land vertically and a rapidly exploring random tree‐based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six‐degree‐of‐freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of state‐of‐the‐art 3D path planning algorithms.

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“…Obstacle avoidance is a crucial aspect of drone operations in urban environments. Pioneering research was performed by Choi and others [15], but they focused only on building avoidance.…”

Section: Obstacle Avoidancementioning

confidence: 99%

Real‐time collision‐free landing path planning for drone deliveries in urban environments

Lee,

Cho,

Jung

2023

ETRI Journal

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“…However, in practice an iteration limit must be enforced due to computational constraints which leads to suboptimal paths [5] as exploration of the environment is prematurely terminated. Another technique that has been used for UAV path planning in urban environments is the artificial potential field (APF) method due to its simplicity and smooth paths [15]. Unfortunately, a well-known drawback of the APF method is that UAVs are occasionally unable to reach goal locations that are behind or too close to an obstacle, and so it is often necessary to introduce additional measures to resolve these "inaccessible goal issues" [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…UAV collision avoidance techniques include rules-based methods, path planning methods, model predictive control (MPC), and rewardsbased methods [4,16]. In urban environments, the artificial potential field (APF) method has also been used for inter-UAV collision avoidance [15] which is less likely to cause inaccessible goal scenarios seeing as other UAVs represent dynamic obstacles. Collision avoidance strategies in UAV systems are broadly classified as being either cooperative or non-cooperative.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous navigation for multiple unmanned aerial vehicles (UAVs) in urban environments

Hughes,

Engelbrecht

2023

MATEC Web Conf.

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This paper presents an autonomous navigation system for multiple unmanned aerial vehicles (UAVs) in urban environments. The navigation system comprises a long-term route planner and a short-term cooperative collision avoidance function. A 3D model of a real urban terrain is used to represent the environment within which the UAVs operate, and the system takes the motion constraints of the UAVs into account when performing planning. The navigation system enables multiple independent UAVs to travel from their departure points to their destinations while predicting and avoiding collisions with one another and with static terrain. Monte Carlo simulations on the system indicate a worst-case route planner replan rate of 11.65 %, route planner failure rate of 9.82×10-4 %, and collision resolution failure rate of 0.12 % for the collision avoidance function.

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Collision Avoidance of Unmanned Aerial Vehicles In an Urban Environment

Cited by 2 publications

References 13 publications

Real‐time collision‐free landing path planning for drone deliveries in urban environments

Real‐time collision‐free landing path planning for drone deliveries in urban environments

Autonomous navigation for multiple unmanned aerial vehicles (UAVs) in urban environments

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