Cooperative search for multi-UAVs via an improved pigeon-inspired optimization and Markov chain approach
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摘要: 針對多無人機在協同搜索過程中存在重復搜索、目標靜止、搜索效率低的問題，提出基于改進鴿群優化和馬爾可夫鏈的多無人機協同搜索方法.首先，建立類似傳感器探測范圍的蜂窩狀環境模型，降低對搜索區域的重復搜索；其次，建立滿足高斯分布的馬爾可夫鏈動態目標運動模型；然后，將柯西擾動引入基本鴿群優化算法的地圖和指南針算子，高斯擾動引入地標算子，同時利用模擬退火機制保留次優個體，進而有效緩減基本鴿群優化算法易陷入局部最優的問題.最后，通過仿真實驗將本文算法與其他群體智能算法進行比較，結果表明新型算法的合理性和有效性.Abstract: Compared with manned aircraft, unmanned aerial vehicles (UAVs) are affordable and convenient for high-risk missions. Therefore, UAVs are increasingly playing an important role in military and civilian fields. Today, UAVs have been exploited to perform special missions carrying some important equipment. However, influenced by the constraints of single UAV's performance and load, it has become a research hotspot that multi-UAVs perform search cooperatively. The process is to minimize the uncertainty of the unknown area and to find the target as much as possible. In terms of cooperation among UAVs, the more effective method based on search map is used. And search process optimization on the basis of distributed model predictive control (DMPC) or traditional swarm intelligence algorithms are adopted, but these methods have some limitations. Due to the behavior of swarm intelligent individual have the characteristics of the decentralization, distribution, and overall self-organization, which match with the requirements of the localization, distribution and robustness of the UAV cooperate search. Nevertheless, the traditional swarm intelligence algorithms have low search efficiency and are easy to fall into local optimum. To solve the problem of repeated search, static targets and low efficiency in cooperative search for multi-UAVs, a method based on improved pigeon-inspired optimization (PIO) and Markov chain was proposed. Firstly, a honeycomb environmental model similar to the sensor detect region was established to reduce repeated search for the area. Secondly, Markov chain with the Gaussian distribution was used to represent dynamic movement of targets. Thirdly, Cauchy mutation and Gaussian mutation were introduced into the map and compass operator and the landmark operator of PIO, respectively. Meanwhile, simulated annealing (SA) mechanism was exploited to reserve the worse individual, which effectively reduced the problem that PIO was easy to fall into local optimum. Finally, the algorithm was compared with other swarm intelligence algorithms through simulation experiments. The results show that the new method is effective and available.
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Key words:
- multi-UAVs /
- cooperative search /
- honeycomb model /
- Markov chain /
- Cauchy mutation /
- Gaussian mutation /
- pigeon-inspired optimization
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圖 3 不同函數隨機一次和10次迭代最優值收斂情況.(a)Rastrigrin; (b)Schaffer
Figure 3. Optimal value convergence of different functions at random times and 10 times of iteration: (a) Rastrigrin; (b) Schaffer
圖 4 目標和環境的初始狀態. (a)目標初始位置; (b)初始環境不確定圖
Figure 4. Initial state of target and environment: (a) initial position of the target; (b) initial environment uncertainty
圖 5 無人機的運動軌跡和覆蓋率.(a)30步的運動軌跡; (b)60步的運動軌跡; (c)搜索范圍覆蓋率
Figure 5. The movement trajectories of unmanned aerial vehicle: (a) the movement trajectory of 30 steps; (b) the movement trajectory of 60 steps; (c) search coverage
圖 6 協同搜索目標數和有效性評估.(a)搜索目標平均數; (b)有效性評估
Figure 6. Effectiveness evaluation of collaborative search: (a) the average target number; (b) effectiveness assessment
久色视频表 1 PIO和MSAPIO參數表
Table 1. Parameters of PIO and MSAPIO
參數 參數定義 數值 應用 NC1 地圖和指南針算子迭代次數 15 鴿群優化擾動模擬退火鴿群優化 NC2 地標算子迭代次數 5 N 鴿子總數 50 R 地圖和指南針因子 0.3 a 柯西分布概率密度參數 1 擾動模擬鴿群優化 μ 高斯分布率密度函數參數(均值) 0 σ 高斯分布概率密度函數參數(方差) 1 K1 地圖和指南針算子的柯西擾動條件 3 K2 地標算子的高斯擾動條件 2 e1 地圖和指南針算子擾動閾值 0.1 e2 地標算子擾動閾值 0.01 T0 初始退火溫度 100 
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參考文獻
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