Vehicle to Vehicle (V2V) communication for Collision avoidance for Multi-copters flying in UTM –TCL4

Chakrabarty, Anjan; Ippolito, Corey A.; Baculi, Joshua E.; Krishnakumar, Kalmanje; Hening, Sebastian

doi:10.2514/6.2019-0690

Cited by 19 publications

(11 citation statements)

References 13 publications

(10 reference statements)

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“…In [7] Chakrabarty, et al they simulated a complete urban operations in a high fidelity simulation environment. they designed a V2V communication protocol and all the vehicles "Drones" participating communicate over this system.…”

Section: Related Workmentioning

confidence: 99%

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

Othman¹,

Megahed²,

Rezka³

et al. 2021

IJACSA

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Directing the antennas of the 5th generation mobile network optimally became a hard and tedious process due to the abundance of the antennas that the 5th generation networks relay on. Also, due to the traditional way of measuring the signal strength of the 5th generation networks. The process that could take weeks of working until it is done. So, the solution is to make an automated process to measure signal strength and to direct antennas using "Drones" rather than human power. This way will ease the process of directing antennas in a quite shorter amount of time which will be some hours instead of weeks. Plus the low cost and the high accuracy achieved. So, a cooperative network between "Drones" and a new communication protocol to support that network will be designed. "Drones" will communicate with each others and with antennas through exchanging messages by "MQTT cloud" using new designed communication protocol. "Raspberry pi" platform will be used as a server to control the direction of antennas. "Drones" will carry a "4G mobile" and a "Raspberry pi" with a "Building Identification System (BIS)" setuped on it. The "(BIS)" gives every building a number and will recognize the entrance of buildings to measure signal strength at every floor. Then, performance analysis metrics (Throughput) will be measured. OMNeT++ "will be used for simulation", and "Raspberry Pi" platform will be used to implement the system and measure the performance of the new Communication Protocol.

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Section: Related Workmentioning

confidence: 99%

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

Othman¹,

Megahed²,

Rezka³

et al. 2021

IJACSA

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“…Data exchange is at the core of UTM where authorized unmanned service suppliers (USS) provide cloud-based services to the different stakeholder. Examples of these services include UAV control [42], efficient and fair unmanned traffic control [12], flight planning and scheduling [23], geofencing [35], path optimization and collision avoidance [11], weather and contingency management [27], [31] , orchestrating of UAV services [10], and supporting the internet-of-drones (IoD) [4].…”

Section: Related Work a Utm Systemsmentioning

confidence: 99%

Integrating Counter-UAS Systems into the UTM System for Reliable Decision Making

Abdulhadi¹,

Shoufan²,

Ruba³

et al. 2021

Preprint

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Despite significant progress, the deployment of UAV technology in commercial and civil applications is still lagging. This is essentially due to the risks associated with drone flights and the lack of coordinated technologies that would mitigate these risks. While Unmanned Aircraft System Traffic Management systems (UTM) are being developed worldwide to enable safe operation, the counter-drone technology operates on an all-enemy basis and regards any sighted drone as a threat. This situation is essentially caused by the lack of information exchange between stakeholders. Without the exchange of relevant information, a counter-drone system can misclassify drones and initiate erroneous interdiction procedures. This paper proposes a system that integrates counter-drone technology into the UTM system for information exchange and coordination using a set of clarification protocols towards accountable response to sighted drones. The system functionality and performance were evaluated by simulation.

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“…Intuitively, picking one 𝑖 at time step 𝑘 results in a configuration (in position space) where the two UAS are separated in one of two ways along one of three axes of motion 4 . For example, if at time step 𝑘 we select 𝑖 with corresponding 𝑀 𝑖 = [0, 0, 1] and 𝑞 𝑖 = −𝛿, it implies that UAS 2 flies over UAS 1 by 𝛿 meters, and so on.…”

Section: Problem Formulation: Mission Aware Uas Collision Avoidancementioning

confidence: 99%

“…Our approach is less restrictive and allows overlaps in the polygons, but performs online collision avoidance on an as-needed basis. A tree search-based planning approach for UAS CA is explored in [4]. The next-gen CA system for manned aircrafts, ACAS-X [18] is a learning-based approach that provides vertical separation recommendations.…”

Section: Introductionmentioning

confidence: 99%

Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme

Rodionova¹,

Pant²,

Kurtz³

et al. 2021

Preprint

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Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-'N-Flying (LNF) a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UAS as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based decision-making, and b) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UAS on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds), and under certain assumptions has failure rates of less than 1% in the worst-case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.

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Vehicle to Vehicle (V2V) communication for Collision avoidance for Multi-copters flying in UTM –TCL4

Cited by 19 publications

References 13 publications

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

A New Communication Protocol for Drones Cooperative Network: 5G Site Survey Case Study

Integrating Counter-UAS Systems into the UTM System for Reliable Decision Making

Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme

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