3D collision avoidance algorithm for Unmanned Aerial Vehicles with limited field of view constraints

Roelofsen, Steven; Martinoli, Alcherio; Gillet, Denis

doi:10.1109/cdc.2016.7798647

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…These numerical results reproduce the classical trajectories as in [30]. The particles move in a straight line to its own target, then when it approaches the collision point, it starts to rotate and finally deviates again to reach the target point.…”

Section: 1supporting

confidence: 70%

On a Three Dimensional Vision Based Collision Avoidance Model

Parzani

Filbet

2017

J Stat Phys

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Abstract. This paper presents a three dimensional collision avoidance approach for aerial vehicles inspired by coordinated behaviors in biological groups. The proposed strategy aims to enable a group of vehicles to converge to a common destination point avoiding collisions with each other and with moving obstacles in their environment. The interaction rules lead the agents to adapt their velocity vectors through a modification of the relative bearing angle and the relative elevation. Moreover the model satisfies the limited field of view constraints resulting from individual perception sensitivity.From the proposed individual based model, a mean-field kinetic model is derived. Simulations are performed to show the effectiveness of the proposed model.

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Section: 1supporting

confidence: 70%

On a Three Dimensional Vision Based Collision Avoidance Model

Parzani

Filbet

2017

J Stat Phys

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“…73 Early on, these methods were used predominantly in avoiding static obstacles, but recently have also been adapted to environments with other moving objects. [74][75][76][77] This approach is inspired by Coulomb's law known in electrostatics, which describes the force between electrically charged particles: the interaction is attractive between charges with opposite signs and repulsive if charges have the same sign. To model correct maneuvers, the UAS and obstacles are set to the same polarity so the UAS will be repulsed from them; whereas the waypoints and the UAS are set to opposite polarities so the UAS will be attracted to planned trajectory.…”

Section: Force Field Methodsmentioning

confidence: 99%

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

Skowron

Chmielowiec

Glowacka

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this study, we discuss recent advances and state-of-the-art sense-and-avoid solutions for small unmanned aircraft systems. The purpose of this paper is to equip the reader with the most up-to-date information on research and commercial efforts aimed at introducing autonomous small unmanned aircraft systems into the shared airspace. We present a broad overview of the regulatory environment, current abilities, and limitations of various sensor types as well as algorithmic and computational aspects of establishing a reliable sense-and-avoid platform. This paper reviews a substantial portion of the literature, focusing on developing reliable sense-and-avoid systems. It brings together information from various sources, including commercial drone and sensor manufacturers, regulatory agencies, as well as basic research performed in this regard. While academic and industrial research activities aimed at developing an effective sense-and-avoid system for small unmanned aircraft systems are intensifying, the current technology does not yet allow for a comprehensive solution that would enable autonomous flight. However, recent advances in hardware miniaturization, increases in computational power, and new algorithmic solutions allow us to remain optimistic about future developments.

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“…Besides, radar usually introduces extra interference due to actively transmitting signals, which will be a big problem if there are larger scales of other wireless devices. Vision‐based technology is an optional method for UAV traffic management, because the UAV can detect intruders and make collision avoidance decisions actively and independently. However, vision processing is computation intensive, limited in coverage angle in the 3D space.…”

Section: Related Workmentioning

confidence: 99%

When UAVs coexist with manned airplanes: Large‐scale aerial network management using ADS‐B

Pan

et al. 2019

Trans Emerging Tel Tech

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Increasing attention has been paid to employing the Automatic Dependent Surveillance‐Broadcast (ADS‐B) technology on unmanned area vehicles (UAVs). The critical challenge therefore emerges, ie, how to effectively schedule the UAVs without interfering the communication. An UAV management framework named uFly is proposed to address this challenge by controlling the total ADS‐B communication traffic over the air. The ADS‐B's interference probability is first modeled. Based on this model, the UAVs' ADS‐B traffic (as well as the number of flying UAVs) should match the manned airplanes' signals. By taking the manned airplanes' signals as the first‐class constraint, uFly then dynamically approves or denies UAVs for flight based on the real‐time number of airplanes. Finally, experiments using different data sets are conducted to verify the superior performance of uFly.

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3D collision avoidance algorithm for Unmanned Aerial Vehicles with limited field of view constraints

Cited by 21 publications

References 12 publications

On a Three Dimensional Vision Based Collision Avoidance Model

On a Three Dimensional Vision Based Collision Avoidance Model

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

When UAVs coexist with manned airplanes: Large‐scale aerial network management using ADS‐B

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