Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Zhang, Honghai; Zhang, Jinpeng; Zhong, Gang; Líu, Hao; Liu, Wenquan

doi:10.1109/access.2022.3210222

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The second layer The collision zero model of the UAV and building are generated as described in a existing study [27], and are elliptical and cylindrical shapes, as shown in Equations ( and (2).…”

Section: Problem Formulationmentioning

confidence: 99%

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Zhang

Zhou

et al. 2023

Drones

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The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time.

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Section: Problem Formulationmentioning

confidence: 99%

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Zhang

Zhou

et al. 2023

Drones

Self Cite

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“…A broader review on the use of UAVs is also done in [17], where authors study the challenges and problems that need to be addressed including UAV traffic management and collision avoidance. Several studies have been done with the goal of detecting conflicts by reducing the computation time associated to this complex problem: [18][19][20][21][22]. However, we consider that the problem is not completely solved if thousands of UAVs have to be controlled in real-time, providing enough prediction time to apply deconfliction techniques.…”

Section: Related Workmentioning

confidence: 99%

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Boronat,

Pérez‐Francisco,

Wubben

et al. 2023

IET Intelligent Trans Sys

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There is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo‐fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real‐time conflict analysis for a large number of UAVs flying in the aerial space of a city (U‐Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U‐Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.

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Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

Wang,

Lin,

Zhang

2024

Drones

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With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement.

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Multivariate Combined Collision Detection for Multi-Unmanned Aircraft Systems

Cited by 3 publications

References 21 publications

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Assessing the limits of centralized unmanned aerial vehicle conflict management in U‐Space

Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

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