基于<i>ε-</i>level蝙蝠算法的无人机三维航迹规划

王福仪; 孟秀云; 张海阔

doi:10.13700/j.bh.1001-5965.2022.0502

基于ε-level蝙蝠算法的无人机三维航迹规划

doi: 10.13700/j.bh.1001-5965.2022.0502

北京理工大学宇航学院，北京 100081

详细信息

通讯作者:
E-mail：mengxy@bit.edu.cn

中图分类号: V249.122⁺.3；TP301.6
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出版历程
- 收稿日期: 2022-06-20
- 录用日期: 2022-08-12
- 网络出版日期: 2023-01-18
- 整期出版日期: 2024-05-29

UAV three-dimensional path planning based on ε-level bat algorithm

School of Aerospace Engineering，Beijing Institute of Technology，Beijing 100081，China

More Information

Corresponding author: E-mail：mengxy@bit.edu.cn

摘要

摘要:
针对复杂地形环境、存在各种威胁和约束的无人机航迹规划问题，提出了一种基于ε-level改进蝙蝠算法的航迹规划算法。根据无人机目标函数和约束条件，建立无人机三维航迹规划模型；其次，针对蝙蝠算法处理高维带约束问题存在的早熟现象，设计了自适应权重系数和迭代阈值以平衡算法的探索能力和开发能力，并结合ε-level比较策略提高算法对非凸非线性约束优化问题的处理能力；设计了转弯半径可变的Dubins曲线用来对航迹进行平滑处理并解决2个航迹点连线贯穿地形的问题。通过仿真实验，并与蝙蝠算法、粒子群优化算法、ε-level粒子群优化算法和ε-level差分进化算法相比较，所提算法在开发能力、稳定性和成功率等方面都表现出较优的性能。
- 无人机航迹规划 /
- 蝙蝠算法 /
- 自适应权重系数 /
- ε-level比较策略 /
- Dubins平滑
Abstract:
To address the problem of complex terrain environment and various threats and constraints, this article proposes a path planning algorithm for UAV based on ε-level improved bat algorithm. First, according to the drone target function and constraints, a three-dimensional path planning model of the UAV is established. Second, in response to the precocious phenomenon in handling the high-dimensional constraints problem of the bat algorithm, the adaptive weight coefficient and iteration threshold are designed to balance the exploration and exploitation capabilities of bat algorithms. Furthermore, by integrating an ε-level comparative strategy, the algorithm's capability to handle issues of non-convex and non-linear constraints is enhanced. Additionally, a three-dimensional Dubins curve with variable turning radius is designed to smooth the path and solve the problem of penetrating the terrain of the two trails. Through simulation experiments and compared with BA, PSO, ε-PSO and ε-DE, the algorithm proposed in this paper shows superior performance in terms of exploitation ability, stability and success rate.
- UAV path planning /
- bat algorithm /
- adaptive weight coefficient /
- ε-level comparison strategy /
- Dubins smoothing

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图 1 坐标转换

Figure 1. Coordinate conversion

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图 2 威胁代价计算

Figure 2. Calculation of threat cost

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图 3 $ r^{0}=0.5 $，$\gamma =0.3 $时发射率随迭代次数变化曲线

Figure 3. Emission rate changes with iterations when $r^{0}=0.5 $, $\gamma =0.3 $

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图 4 航迹贯穿地形

Figure 4. Track penetrates the terrain

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图 5 Dubins 曲线

Figure 5. Dubins curve

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图 6 三维 Dubins 曲线

Figure 6. Three -dimensional Dubins curve

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图 7 转弯半径可变的 Dubins 航迹平滑流程

Figure 7. Dubins track smoothing process with variable turning radius

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图 8 平滑后的航迹

Figure 8. Track after smooth

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图 9 不同算法得到的 UAV 航迹

Figure 9. UAV track obtained by different algorithms

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图 10 航迹剖面图

Figure 10. Track profile

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图 11 航迹代价收敛曲线

Figure 11. Convergence curves of path cost

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表 1 任务和威胁信息

Table 1. Task and threat information km

威胁类型	威胁范围
导弹	([190,180],90); ([650,460],80)
高射炮	([480,540],80); ([850,650],70)
雷达	([500,210],150); ([630,760],150)
禁飞区	([100,360],[380,600]); ([710,890],[170,370])

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表 2 算法性能统计结果

Table 2. Algorithm performance statistics

算法	最优/10⁶	期望/10⁶	最差/10⁶	标准差/10⁵	成功率/%
$ \varepsilon \text{-} \mathrm{IBA} $	1.249	1.388	1.558	0.7850	98
BA	1.377	2.632	3.359	5.906	36
PSO	1.264	2.726	3.658	6.525	28
$ \varepsilon \text{-} {{\mathrm{PSO}} } $	1.260	1.523	1.869	1.583	64
$ \varepsilon \text{-} {{\mathrm{DE}}} $	1.412	1.755	2.039	2.329	54

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