Cooperative Path Optimization for Multiple UAVs Surveillance in Uncertain Environment

Liu, Daqian; Bao, Weidong; Zhu, Xiaomin; Fei, Bowen; Men, Tong; Xiao, Zhenliang

doi:10.1109/jiot.2021.3125784

Cited by 19 publications

(5 citation statements)

References 32 publications

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“…Recently, unmanned aerial vehicles (UAV) have been widely applied to a variety of fields due to their advantages of high flexibility, low operating cost, and ease of deployment. In the military field, UAVs have become an important part of modern warfare and can be used for missions such as reconnaissance (Qin et al, 2021 ), strikes (Chamola et al, 2021 ), and surveillance (Liu et al, 2021 ), reducing casualties and enhancing combat efficiency. In the field of agriculture, UAVs have good applications in plant protection (Xu et al, 2019 ; Chen et al, 2021 ), agricultural monitoring (Zhang et al, 2021 ), and so on, improving the efficiency and precision of agricultural operations.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

Kong,

Zhou,

et al. 2024

Front. Neurorobot.

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Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

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Section: Introductionmentioning

confidence: 99%

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

Kong,

Zhou,

et al. 2024

Front. Neurorobot.

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“…Regarding the application aspect of Airborne Radar Network (ARN) formation: In terms of security, during the execution of operational missions, radar-equipped airborne platforms form cluster formations, which can be perceived as a spatially separated yet logically integrated virtual detection platform. This detection pattern facilitates complementary advantages among individual entities in the cluster, whereby the destruction of any single node does not affect the normal operation of other nodes, thereby enhancing the survivability of the cluster in complex battlefield environments 12 , 13 . In terms of detection performance, any member within a group can act as a transmitter while other members function as receivers.…”

Section: Introductionmentioning

confidence: 99%

Formation optimization of airborne radar coordinated detection system using an improved Artificial Fish Swarm Algorithm

Li,

Meng,

Meng

et al. 2024

Sci Rep

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In modern air combat, collaborative detection and engagement among multiple aircraft have gradually become a predominant combat approach. In response to the challenges posed by modern stealth aircraft, although their external factors such as coatings significantly reduce the chances of enemy detection, once these stealth aircraft activate their radar systems, they become susceptible to detection. Therefore, an application model has been proposed to mitigate enemy detection of our stealth aircraft through a collaborative approach. The underlying principle involves employing the concept of multi-aircraft collaboration, where the aircraft are divided into transmitters and receivers. The transmitters emit radar waves while the receivers are responsible for receiving these waves. This approach effectively mitigates the increased probability of enemy detection resulting from the activation of our receivers' radar systems. The optimization problem we aim to address is determining the optimal formation configuration for cooperative flight, specifically a formation with a specific configuration that maximizes the detectable range. This optimization problem is known as the configuration optimization problem for Airborne Radar Network with Separate Transmitting and Receiving (ARN-STAR). Existing methods for this problem typically suffer from limitations in either effectiveness or efficiency. To overcome these limitations, we propose an optimized configuration method based on an improved Artificial Fish Swarm Algorithm (IFSA) for ARN-STAR. Firstly, leveraging the distribution characteristics of the target radar wave’s spatial scattering and the concept of dual-radar spatial diversity, we establish a mathematical model and an optimization objective function for ARN-STAR. Secondly, to address efficiency concerns, we optimize the computational process using the IAFS, successfully improving the speed of computation. To address the issue of effectiveness, we introduce adaptive adjustments to the movement step size of the artificial fish and improve the implementation of the three behavioral modes, thereby avoiding local optima and enhancing the accuracy of finding the optimal configuration. Finally, using our self-developed multi-aircraft collaborative simulation platform, we apply the improved AFSA to obtain the optimal formation configuration scheme and compare it with other methods. Simulation results demonstrate that our proposed method effectively solves the problem of finding the optimal formation configuration in multi-aircraft collaborative detection scenarios with “one transmission and multiple receptions.” It overcomes the low computational efficiency associated with traditional methods while maintaining good accuracy. This approach enables the enhancement of overall combat capabilities while ensuring the safety of our aircraft to the greatest extent possible. It should be noted that the scenarios discussed in this study are at the configurational configuration level between UAVs, rather than involving the design of the UAVs combat control system itself.

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“…Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping [1][2][3][4][5][6], ecological monitoring [7], rescue [8], military, and other fields [9][10][11][12]. However, autonomy and intelligence are still lacking in these scenarios.…”

Section: Introductionmentioning

confidence: 99%

Robust Planning System for Fast Autonomous Flight in Complex Unknown Environment Using Sparse Directed Frontier Points

Zhao

Dai

et al. 2023

Drones

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Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in cluttered environments. However, it remains a challenge to efficiently generate a high-quality trajectory for flight tasks with a high success rate. In this paper, a robust planning framework is proposed, which can stably support autonomous flight tasks in complex unknown environments with limited onboard computing resources. Firstly, we propose the directed frontier point information structure (DFP), which can roughly capture the frontier information of the explored environment. The planning direction of a local planner can be evaluated and rectified efficiently based on the DFP to avoid falling into traps with limited cost. Secondly, an adaptive fusion replanning method is designed to generate a high-quality trajectory efficiently by incorporating two optimization methods with different characteristics, which can both take advantage of different optimization methods while avoiding disadvantages as much as possible, but also adjust the focus of the optimization according to the actual situation to improve the success rate of the planning method. Finally, sufficient comparison and evaluation experiments in simulation environments are presented. Experimental results show the proposed method has better performance, especially in terms of adaptability and robustness, compared to typical and state-of-the-art methods in unknown complex scenarios. Moreover, the proposed system is integrated into a fully autonomous quadrotor, and the effectiveness of the proposed method is further evaluated by using the quadrotor in real-world environments.

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Cooperative Path Optimization for Multiple UAVs Surveillance in Uncertain Environment

Cited by 19 publications

References 32 publications

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments

Formation optimization of airborne radar coordinated detection system using an improved Artificial Fish Swarm Algorithm

Robust Planning System for Fast Autonomous Flight in Complex Unknown Environment Using Sparse Directed Frontier Points

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