| 摘要: |
| 面向无人机在线/离线航迹规划应用,针对传统乌贼算法的长时搜索局域化及精度变差问题,提出了一种联合修正的自适应乌贼路径搜索算法.首先,提出联合混沌扰动与变异学习的混合调节机制来扩充乌贼搜索深度,以提高搜索精度；然后,引入自适应权重机制来减小乌贼搜索范围,以提高搜索效率；同时引入适应度自动筛选机制来改善乌贼种群多样性,以防止陷入局部最优.通过6个基准函数测试验证了所提算法的有效性与先进性,最后对所提算法进行不同场景下的航迹规划仿真验证.针对离线航迹规划,所提算法规划航迹成功率高达100%,规划航迹最接近全局最优,其航程均值相比传统乌贼算法可缩减7.3 units,比粒子群算法缩减可达28.3 units.仿真结果表明:所提算法全局规划性能和搜索精度显著增强,同时随着场景复杂度的提高,其航迹优化效果更加显著；针对在线航迹规划,首先将全局路径规划问题转化为若干个航迹分段的规划,然后引入启发式方法确定分段节点.仿真结果显示所提算法满足实时性要求,规划航迹精度高,进一步验证了所提算法的有效性. |
| 关键词: 在线/离线航迹规划 自适应乌贼算法 扰动与变异 自适应权重 混沌搜索 |
| DOI：10.11918/j.issn.0367-6234.201805004 |
| 分类号:V249；TP18 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61271317),航天支撑技术基金(15GFZ-JJ02-07) |
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| Adaptive cuttlefish algorithm for UAV path planning |
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QIAN Zhouyuan,LEI Ming
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(School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China)
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| Abstract: |
| To solve the UAV offline and online path planning problem, an adaptive cuttlefish algorithm with joint modification is proposed considering that the traditional cuttlefish algorithm may be trapped in local optimum and has low precision after long search. The regulatory mechanism combined with chaos perturbation and mutation learning is proposed to strengthen local search to improve search precision. Adaptive weight mechanism is introduced to diminish search space and ensure search efficiency. The auto filter mechanism based on individual fitness was used to adjust population diversity and to break away from local optimum. The proposed algorithm was firstly verified by tests of six benchmark functions, and then verified by path planning simulations in different environments. For offline path planning, success rate of the proposed algorithm reaches up to 100%, and paths planned by the proposed algorithm were closest to global optimum. The average track length of the proposed algorithm could be 7.3 units shorter than traditional cuttlefish algorithm, and it could reach 28.3 units shorter than particle swarm optimization. The simulation results show that the global search performance and the search precision of the proposed algorithm were significantly improved. Furthermore, as the environment became more complex, the improvement effects were more remarkable. For online path planning, the global path planning was transformed to the planning of several segmental paths, and a heuristic method was used to select the segmental goal. Simulation results show that the proposed algorithm met the real-time requirement and has high precision. The effectiveness of the proposed algorithm was further verified. |
| Key words: online/offline path planning adaptive cuttlefish algorithm disturbance and mutation adaptive weight chaos search |
