Multiple-Aircraft-Conflict Resolution Under Uncertainties

Erzberger, Heinz; Liu, Yongming

doi:10.2514/1.g005825

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…Zeng et al combined the ant colony algorithms and the A* algorithm to solve the unmanned ground vehicle (UGV) scheduling planning problem, avoiding conflicts during simultaneous path planning for UGVs at a lower cost [6]. Zhao et al considered collision probability and the intention information of intruders, using the A* algorithm to optimize trajectory planning to avoid collision risks [7]. Yun et al applied the enhanced D* lite algorithm in the field of robot path navigation in unknown dynamic environments [8].…”

Section: Related Prior Workmentioning

confidence: 99%

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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: Related Prior Workmentioning

confidence: 99%

“…(2) Building collision avoidance reward: when there are obstacles in the sector of the UAV pair, it is necessary to ensure that the UAVs avoid colliding with buildings while flying to their destinations; therefore, we need to balance the two tasks, and the reward function at this time is shown in Equation (7).…”

Section: Reward Function Designmentioning

confidence: 99%

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Zhang

Zhou

et al. 2023

Drones

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show abstract

“…We can easily know that the complexity of a single integral is 𝑂(𝑛 𝑙𝑜𝑔 𝑛) by Newton-Leibniz equation shown in Equation (7), so the complexity of a triple independent variable integral is 𝑂(𝑛 (𝑙𝑜𝑔 𝑛) ) , as shown in Equation (8).…”

Section: Time Complexity Analysismentioning

confidence: 99%

“…Dang et al have conducted targeted research on conflict detection in specific scenarios (limited airspace [4], parallel flight [5], and coverage range of conflict areas [6]). At the same time, Dudoit et al [7][8] have taken care of both the external environment (such as wind and rain) and human factors, making the prediction of collision risk more comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Aviation Collision Risk Warning for Non-Tower Airport Based on Trajectory Prediction

Yang,

Zeng,

Liu

et al. 2023

J. Phys.: Conf. Ser.

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With the development of the civil aviation industry, the throughput of airports continues to increase, including non-tower airports (small airports without the ability to actively communicate with aircraft). These non-tower civil airports can handle small aircraft such as personal aircraft well and lay a solid foundation for future automated aircraft. However, there is a high collision risk in taking off or landing because the probability of avoiding risks highly relies on the pilot’s flight proficiency and experience. To address this problem, we studied the collision risk of aircraft in non-tower airports and proposed a collision risk warning method based on trajectory prediction. The proposed method obtains the probability of future collisions by learning the flight patterns of all aircraft in non-tower airports, the impact of environmental wind speed on pilot behavior, and the interaction patterns between aircraft. At the same time, we conducted in-depth optimization of the probability integration calculation process under a conditional threshold to reduce the time complexity greatly. The simulation experiment results on non-tower airports show that the designed method for collision risk warning can effectively address the aircraft collision prediction problem in non-tower airports, and outperform other benchmark learning methods. It has significant guiding implications for improving airport intelligence and aircraft automation in the future.

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“…Besides abovementioned articles, it is important to note a recent research conducted by Zhao et al in [23], where a new multiple-aircraft-conflict resolution method was proposed that could be extended to tackle the problem of convective weather cell avoidance as well. The method is based on a probabilistic conflict risk map (image) that makes the conflict resolution problem equivalent to the problem of finding a path to avoid risks.…”

Section: A Weather Avoidance System For Near-term Trajectory-based Op...mentioning

confidence: 99%

Conflict resolution in the case of convective weather cell circumvention

Bogdanović

Mao

2023

J Big Data

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This research analyzes the area required for the conflict resolution between aircraft in two flows impacted by a convective weather cell (CWC). The CWC is introduced as a constrained area, forbidden to flight through, which affects the air traffic flows. Prior the conflict resolution, two flows and their intersection are relocated away from the CWC area (thus enabling circumvention of the CWC), which is followed by a tuning of the relocated flows intersection angle in order to create the minimal size of the conflict zone (CZ—a circular area centered at the intersection of two flows, which provides aircraft enough space to completely resolve the conflict within). Therefore, the essence of the proposed solution is in providing conflict free trajectories for the aircraft in intersecting flows that are affected by the CWC, with the goal of minimizing the CZ size, so the finite occupied airspace for the conflict resolution and the CWC circumvention could be reduced. Compared to the best solutions and current industry practice, this article is focused in reduction of the airspace required for aircraft to aircraft and aircraft to weather conflict resolution, and not to distance travelled, time savings, and fuel consumption minimization. The conducted analysis in the MicrosoftExcel2010 confirmed the relevance of the proposed model and demonstrated variations in efficiency of the utilized airspace. The proposed model's transdisciplinary nature makes it potentially applicable in other fields of study, such as the conflict resolution between unmanned aerial vehicles (UAVs) and fixed objects like buildings. Building on this model and taking in consideration large and complex data sets, such as weather related data and flight data (aircraft position, speed, and altitude), we believe it is possible to conduct more sophisticated analyses that would take advantage of Big Data.

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Multiple-Aircraft-Conflict Resolution Under Uncertainties

Cited by 12 publications

References 35 publications

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Aviation Collision Risk Warning for Non-Tower Airport Based on Trajectory Prediction

Conflict resolution in the case of convective weather cell circumvention

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