A Decision-making Method for UAV Conflict Detection and Avoidance System

TANG Xinmin; LI Shuai; GU Junwei; GUAN Xiangmin

doi:10.11999/JEIT240503

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TANG Xinmin, LI Shuai, GU Junwei, GUAN Xiangmin. A Decision-making Method for UAV Conflict Detection and Avoidance System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240503

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TANG Xinmin, LI Shuai, GU Junwei, GUAN Xiangmin. A Decision-making Method for UAV Conflict Detection and Avoidance System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240503

TANG Xinmin, LI Shuai, GU Junwei, GUAN Xiangmin. A Decision-making Method for UAV Conflict Detection and Avoidance System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240503

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A Decision-making Method for UAV Conflict Detection and Avoidance System

doi: 10.11999/JEIT240503

1.
Key Laboratory of Urban Air Traffic System Technology and Equipment, Civil Aviation University of China, Tianjin 300300, China
2.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
Key Laboratory of Civil Aviation General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China

Funds: The National Natural Science Foundation of China (52072174, 61773202), The Open Fund for the Key Laboratory of Civil Aviation General Aviation Operations of China Civil Aviation Management Cadre College (CAMICKFJJ-2019-04)

Received Date: 2024-06-19
Rev Recd Date: 2024-09-07

Available Online: 2024-09-28

Abstract

Abstract

A novel approach is proposed to integrate the Detection and Avoidance Alerting Logic for Unmanned Aircraft Systems (DAIDALUS) with the Markov Decision Process (MDP) to address the decision-making challenges associated with collision avoidance in Unmanned Aerial Vehicle (UAV) Detection and Avoidance (DAA) systems. The guidance logic inherent in the DAIDALUS algorithm is utilized to compute drone collision avoidance strategies based on the current state space. These strategies are subsequently employed as the action space for the MDP, with suitable reward functions and state transition probabilities defined to establish an MDP model. The model is then used to investigate the effects of various discount factors on the UAV flight collision avoidance process. The simulation results show that compared to DAIDALUS, the efficiency of this method has increased by 27.2%;when the discount factor is set to 0.99, it can balance long-term and short-term returns; The net intrusion rate is 5.8%, and the closest distance between the threatening aircraft and the local aircraft is 343 meters, which can meet the requirements of collision avoidance during drone flight.
- UAV systems,
- Detect and Avoid (DAA),
- Markov Decision Process (MDP),
- Reward function

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References(22)

References

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