无人机任务规划与优化方法研究
本专题聚焦无人机技术在任务规划与优化中的前沿研究，涵盖了多无人机协同作战、智能攻防对抗、任务调度优化、导航抗干扰、目标识别、作战建模以及资源配置效率等关键领域。专题中的研究探讨了异构无人机编队在复杂战场环境下的路径规划，提出了网络化无人机集群任务调度的创新方法，展示了智能化无人机在攻防对抗中的策略优化。同时，专题还深入研究了无人机在复杂电磁环境中的导航抗干扰技术，以及结合元学习和自注意力机制的小样本目标识别模型。此外，专题探讨了提高无人机集群作战建模效率的计算方法和无人机训练资源配置效率的综合评价体系。通过系统的理论分析和实际应用验证，这些研究不仅为无人机技术的发展提供了理论支持和实践指导，也为相关领域的研究者提供了新的研究思路和应用参考。
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2022, 44(4):1-12.
DOI: 10.11887/j.cn.202204001
Abstract:
The basic principle, representative methods, and state-of-the-art research of the sub-module related research within the general framework of cooperative maneuvering flight planning from single UAV (unmanned aerial vehicle) maneuvering flight to multi-UAV cooperative planning were introduced. It mainly included real-time navigation map construction, discrete-space path planning, continuous-space trajectory planning, hybrid planning based on discrete-space and continuous-space, and multi-courses/trajectories cooperative planning. The next research directions were proposed based on the major technologies of the planning framework.
The basic principle, representative methods, and state-of-the-art research of the sub-module related research within the general framework of cooperative maneuvering flight planning from single UAV (unmanned aerial vehicle) maneuvering flight to multi-UAV cooperative planning were introduced. It mainly included real-time navigation map construction, discrete-space path planning, continuous-space trajectory planning, hybrid planning based on discrete-space and continuous-space, and multi-courses/trajectories cooperative planning. The next research directions were proposed based on the major technologies of the planning framework.
2023, 45(4):45-54.
DOI: 10.11887/j.cn.202304006
Abstract:
Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method, the idea of “computation-for-communication” and its corresponding distributed task scheduling method were proposed. By analyzing explicit and implicit conflicting tasks, a set of task-related agents was established. A local optimization method based on task suppression was proposed to resolve some task conflicts in advance, so as to reduce the number of algorithm iterations. An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization. Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario. The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method, the proposed method can obtain fewer iterations, shorter convergence time and better scheduling performance.
Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method, the idea of “computation-for-communication” and its corresponding distributed task scheduling method were proposed. By analyzing explicit and implicit conflicting tasks, a set of task-related agents was established. A local optimization method based on task suppression was proposed to resolve some task conflicts in advance, so as to reduce the number of algorithm iterations. An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization. Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario. The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method, the proposed method can obtain fewer iterations, shorter convergence time and better scheduling performance.
2023, 45(1):157-166.
DOI: 10.11887/j.cn.202301018
Abstract:
Based on the concept of the intelligent combat of UAV (unmanned aerial vehicle) swarms, the UAV swarms intelligent combat simulation environment was established. Aiming at the problem that it is difficult to accurately control the speed and attack angle of UAVs in the confrontation process through reward signals in traditional reinforcement learning algorithms, the RIC-MADDPG (rule and intelligence coupling constrained multi-agent deep deterministic policy gradient) algorithm was proposed. The algorithm uses rules to constrain the actions of UAVs in reinforcement learning. The simulation results show that the wining-rate of red UAV swarm, trained by the method based on the RIC-MADDPG, can be improved from 53% to 79%. This proves that the strategy of "agent training—problem finding—rule making—agent training again—problem finding again—rule making again" is effective for the optimization of agent combat strategy. The research results can be a reference for establishing the training system of the intelligent combat strategy of UAV swarms and conducting the research of swarm tactics coupling rule and intelligence.
Based on the concept of the intelligent combat of UAV (unmanned aerial vehicle) swarms, the UAV swarms intelligent combat simulation environment was established. Aiming at the problem that it is difficult to accurately control the speed and attack angle of UAVs in the confrontation process through reward signals in traditional reinforcement learning algorithms, the RIC-MADDPG (rule and intelligence coupling constrained multi-agent deep deterministic policy gradient) algorithm was proposed. The algorithm uses rules to constrain the actions of UAVs in reinforcement learning. The simulation results show that the wining-rate of red UAV swarm, trained by the method based on the RIC-MADDPG, can be improved from 53% to 79%. This proves that the strategy of "agent training—problem finding—rule making—agent training again—problem finding again—rule making again" is effective for the optimization of agent combat strategy. The research results can be a reference for establishing the training system of the intelligent combat strategy of UAV swarms and conducting the research of swarm tactics coupling rule and intelligence.
2022, 44(6):109-116.
DOI: 10.11887/j.cn.202206014
Abstract:
In the complex electromagnetic environment of the battlefield, satellite navigation receivers are susceptible to EMI (electromagnetic interference) and cannot be positioned. In response to this phenomenon, a method for evaluating the EMI situation of satellite navigation receivers based on unmanned aerial vehicle′s environmental perception was proposed. When the navigation receiver was not interfered, the characteristic parameters of the EMI and the receiving state of the navigation receiver were used as the input of the prediction. When the receiver tracking loop was lost, the effect threshold was used as the observation target value to establish the XGBoost prediction model. On this basis, the rank of the EMI situation of the navigation receiver was given, and the situation assessment method of the navigation receiver under single-source or dual-source were proposed. Compared with the prediction methods of Gaussian processes for regression and support vector regression, the results show that the XGBoost method has the better prediction accuracy. According to this prediction method, the comprehensive utilization of the technology schemes and the tactical schemes is beneficial to improving the adaptability of unmanned aerial vehicles in complex electromagnetic environments.
In the complex electromagnetic environment of the battlefield, satellite navigation receivers are susceptible to EMI (electromagnetic interference) and cannot be positioned. In response to this phenomenon, a method for evaluating the EMI situation of satellite navigation receivers based on unmanned aerial vehicle′s environmental perception was proposed. When the navigation receiver was not interfered, the characteristic parameters of the EMI and the receiving state of the navigation receiver were used as the input of the prediction. When the receiver tracking loop was lost, the effect threshold was used as the observation target value to establish the XGBoost prediction model. On this basis, the rank of the EMI situation of the navigation receiver was given, and the situation assessment method of the navigation receiver under single-source or dual-source were proposed. Compared with the prediction methods of Gaussian processes for regression and support vector regression, the results show that the XGBoost method has the better prediction accuracy. According to this prediction method, the comprehensive utilization of the technology schemes and the tactical schemes is beneficial to improving the adaptability of unmanned aerial vehicles in complex electromagnetic environments.
2024, 46(1):32-41.
DOI: 10.11887/j.cn.202401004
Abstract:
SEAD(suppression of enemy air defenses) is a typical application scenario of multi-UAV cooperation. Based on the characteristics of this scenario, the number of different types of UAV was also used as a decision variable in the task planning problem, fully characterizing the various constraints of the target, mission, and UAV, and establishing a heterogeneous UAV formation path problem model. A two-layer joint optimization method was designed to solve the model:the upper layer was designed with the task connection impact indicator to accurately assess the quantitative requirements of various types of UAVs and guide UAVs configuration adjustments; the lower layer improved the genetic algorithm, which can efficiently handle multiple coupling constraints and can accurately adjust the mission plan in conjunction with UAV quantity changes. The two layers coordinate with each other to obtain a UAV configuration and mission execution plan that meet the requirements. Simulation results show that the method can obtain a reasonable UAV configuration plan without traversing various UAV configurations, while obtaining an efficient and feasible mission execution plan.
SEAD(suppression of enemy air defenses) is a typical application scenario of multi-UAV cooperation. Based on the characteristics of this scenario, the number of different types of UAV was also used as a decision variable in the task planning problem, fully characterizing the various constraints of the target, mission, and UAV, and establishing a heterogeneous UAV formation path problem model. A two-layer joint optimization method was designed to solve the model:the upper layer was designed with the task connection impact indicator to accurately assess the quantitative requirements of various types of UAVs and guide UAVs configuration adjustments; the lower layer improved the genetic algorithm, which can efficiently handle multiple coupling constraints and can accurately adjust the mission plan in conjunction with UAV quantity changes. The two layers coordinate with each other to obtain a UAV configuration and mission execution plan that meet the requirements. Simulation results show that the method can obtain a reasonable UAV configuration plan without traversing various UAV configurations, while obtaining an efficient and feasible mission execution plan.
2022, 44(4):13-21.
DOI: 10.11887/j.cn.202204002
Abstract:
Aiming at the problem of occlusion and confusion targets recognition for UAV (unmanned air vehicle) under small sample conditions, a target recognition model integrating self-attention mechanism and few-shot learning was proposed. On the basis of using the idea of meta learning to obtain the ability of few-shot learning, the self-attention mechanism to learn the context dependence between the internal parts of the target was introduced into the model, so as to enhance the target representation ability and solve the problem of insufficient effective features in the case of occlusion and confusion. In order to verify the effect of the model, the occlusion and confusion target datasets were constructed by further processing the reference datasets and UAV aerial photography data, and different occlusion degrees and background confusion rates were set. Through the verification on different datasets and compared with the deep learning model, the proposed model is proved to possess higher learning efficiency and recognition accuracy.
Aiming at the problem of occlusion and confusion targets recognition for UAV (unmanned air vehicle) under small sample conditions, a target recognition model integrating self-attention mechanism and few-shot learning was proposed. On the basis of using the idea of meta learning to obtain the ability of few-shot learning, the self-attention mechanism to learn the context dependence between the internal parts of the target was introduced into the model, so as to enhance the target representation ability and solve the problem of insufficient effective features in the case of occlusion and confusion. In order to verify the effect of the model, the occlusion and confusion target datasets were constructed by further processing the reference datasets and UAV aerial photography data, and different occlusion degrees and background confusion rates were set. Through the verification on different datasets and compared with the deep learning model, the proposed model is proved to possess higher learning efficiency and recognition accuracy.
2022, 44(4):43-51.
DOI: 10.11887/j.cn.202204005
Abstract:
In order to solve the problem of low computing speed in the process of state transition in the analytical modeling of UAV (unmanned aerial vehicle) swarm operation, a fourth-order Runge-Kutta method based on the row compressed storage was proposed. The UAV swarm operation process was divided into three stages according to the UAV swarm operation style, and continuous time Markov chain model was established for the state transition process of UAV swarm operation in stages. In the meantime, taking the reliability of UAV swarm to complete combat mission as the solving index, the fourth-order Runge-Kutta method was used to solve the Markov model, and the method based on row compressed storage was used to optimize the solving rate owing to the sparsity feature of the rate transfer matrix. Simulation results show that the established continuous time Markov chain model has better effectiveness and feasibility than other models. At the same time, compared with other algorithms, the proposed algorithm has higher computing speed and better reliability requirements to meet the accuracy of results, which further shows the superiority of it.
In order to solve the problem of low computing speed in the process of state transition in the analytical modeling of UAV (unmanned aerial vehicle) swarm operation, a fourth-order Runge-Kutta method based on the row compressed storage was proposed. The UAV swarm operation process was divided into three stages according to the UAV swarm operation style, and continuous time Markov chain model was established for the state transition process of UAV swarm operation in stages. In the meantime, taking the reliability of UAV swarm to complete combat mission as the solving index, the fourth-order Runge-Kutta method was used to solve the Markov model, and the method based on row compressed storage was used to optimize the solving rate owing to the sparsity feature of the rate transfer matrix. Simulation results show that the established continuous time Markov chain model has better effectiveness and feasibility than other models. At the same time, compared with other algorithms, the proposed algorithm has higher computing speed and better reliability requirements to meet the accuracy of results, which further shows the superiority of it.
2022, 44(4):204-212.
DOI: 10.11887/j.cn.202204023
Abstract:
In order to reduce the subjective one-sided influence of empowerment process of the traditional hierarchical analysis method, a comprehensive AHP(analytic hierarchy process) and Arena simulation modeling technology was proposed to evaluate the efficiency of the allocation of UAV(unmanned aerial vehicle) training resources. Based on flight training process analysis, the factors influencing the efficiency of training resource allocation and efficiency evaluation index were determined, a hierarchical comprehensive evaluation system was established, and the comprehensive evaluation steps of AHP-Arena were summarized. Arena modeling software was used to realize the dynamic simulation system of the training process and verify the validity, the single-variable numerical simulation method and the mean variance decision-making method were used to complete the objective empowerment of the influencing factors, and the comprehensive evaluation coefficient was obtained by linear weighting comprehensive method, so as to implement resource allocation efficiency evaluation and selection of the optimal program.The practical application shows the effectiveness of the method. Meanwhile, this method can be extended to other similar training processes, and can also provide references for other multi-factor multi-indicator evaluation decision-making processes.
In order to reduce the subjective one-sided influence of empowerment process of the traditional hierarchical analysis method, a comprehensive AHP(analytic hierarchy process) and Arena simulation modeling technology was proposed to evaluate the efficiency of the allocation of UAV(unmanned aerial vehicle) training resources. Based on flight training process analysis, the factors influencing the efficiency of training resource allocation and efficiency evaluation index were determined, a hierarchical comprehensive evaluation system was established, and the comprehensive evaluation steps of AHP-Arena were summarized. Arena modeling software was used to realize the dynamic simulation system of the training process and verify the validity, the single-variable numerical simulation method and the mean variance decision-making method were used to complete the objective empowerment of the influencing factors, and the comprehensive evaluation coefficient was obtained by linear weighting comprehensive method, so as to implement resource allocation efficiency evaluation and selection of the optimal program.The practical application shows the effectiveness of the method. Meanwhile, this method can be extended to other similar training processes, and can also provide references for other multi-factor multi-indicator evaluation decision-making processes.
