Deep reinforcement learning for UAV swarm rendezvous behavior

Yao-zhong, Zhang; Li, Yike; Wu, Zhuoran; Xu, Jialin

doi:10.23919/jsee.2023.000056

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The above equation covers 10-dimensional vectors exhibiting different magnitudes and different degrees of sparsity, such that the degree of value change in 1 reconnaissance cycle is different. To be able to be sensitive to the changes of each vector and accelerate the convergence speed of the algorithm, the 10-dimensional vectors need to be normalised, as expressed in Equation (18). In Scouter, the mission area of the UAV is a rectangular area of 75 km (length) � 75 km (width) � 6 km (height), X max is 37.5 km, X min is −37.5 km, Y max is 37.5 km, Y min is −37.5 km; H max is 12 km, H min is 6 km, P u;min is the UAV reconnaissance payload sensitivity.…”

Section: Reward Shapingmentioning

confidence: 99%

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Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

Zhang,

Chen,

Yan

et al. 2023

IET Radar Sonar & Navi

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The integration of electronic reconnaissance platforms and payloads in the electromagnetic and spatial domains of cognition is necessary for the successful execution of cognitive electronic reconnaissance missions. However, in partially observable environments, platforms and payloads interact in separate operational domains, making it a challenge for the model. The authors create a theoretical model of cognitive electronic reconnaissance, present an analysis method of model elements and derives adequate requirements for optimal radiation source sorting or recognition. Then, a 3D cognitive reconnaissance simulator ‘Scouter’ to simulate the dynamic interaction process of space electronic reconnaissance missions is developed and a deep reinforcement learning framework to optimise the electronic reconnaissance strategies of Unmanned Aerial Vehicle (UAV) is designed. For the first time, a method for judging reconnaissance area coverage using a behavioural angle is proposed. This method enables a deep reinforcement learning‐based UAV with better reconnaissance behaviours and to realise the stable targeting of the target by the receiving antenna beam during reconnaissance. Moreover, the fast convergence of UAV agent training is ensured through the normalisation of partially observable status and reward shaping. Verified by adequate numerical simulations, a trained UAV can complete a 100 reconnaissance cycles mission with a completion rate of 91.9%. Even after the reconnaissance cycles are prolonged to 600, the mission completion rate can still be maintained at 78.5%. The most satisfying aspect is that the UAV comprehends the idea of reconnaissance and behaves in a way that is typical of it, such as by reciprocating or circling the edge of the radar's detecting power.

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Section: Reward Shapingmentioning

confidence: 99%

“…In recent years, deep reinforcement learning technology has proved to be successful in many difficult decision-making missions [18][19][20][21]. The trained intelligent agents impress CEW researchers with their excellent decision-making abilities.…”

Section: Introductionmentioning

confidence: 99%

Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

Zhang,

Chen,

Yan

et al. 2023

IET Radar Sonar & Navi

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“…Samir et al [15] combined DRL with joint optimization to achieve improved learning efficiency, although changes to the dynamic environment can hinder the implementation of this strategy. Zhang et al [16] investigated a double deep Q-network (DQN) framework for long period UAV swarm collaborative tasks and designed a guided reward function to solve the convergence problem caused by the sparse returns of long period tasks. Huda et al [17] investigated a surveillance application scenario using a hierarchical UAV swarm.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance Restoration

Wu,

Geng,

Ren

et al. 2023

Sensors

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Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios.

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Scalable and cohesive swarm control based on reinforcement learning

Blais,

Akhloufi

2024

Cognitive Robotics

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Deep reinforcement learning for UAV swarm rendezvous behavior

Cited by 4 publications

References 10 publications

Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

Research on single target cognitive electronic reconnaissance strategy for unmanned aerial vehicle

Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance Restoration

Scalable and cohesive swarm control based on reinforcement learning

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