基于动态自适应蚁群算法的大型无人机智能巡检路径规划系统设计

Design of intelligent inspection path planning system for large uav based on dynamic adaptive ant swarm algorithm

摘要:

无人机巡检作为电力、石油等领域高效安全的检测手段,其路径规划效率直接影响巡检效果。针对传统算法在动态避障和实时优化方面的不足,文章设计了一种无人机巡检路径规划系统。系统采用多频段信号融合技术和低功耗电路设计,优化了北斗导航模块的抗干扰性能,同时构建智能电源管理系统以延长续航时间。通过引入障碍物排斥权重和新启发因子改进蚁群算法,结合动态障碍物斥力势场模型,显著提升了无人机在复杂环境中的避障能力与路径优化质量。实验结果表明,在简单环境下改进算法最优路径长度为186.54米,复杂环境下为200.32米,较传统算法分别缩短21.8%和19.3%。动态场景测试显示,改进算法在路径安全性、收敛速度和能耗效率方面表现优异,最小避障距离达5.2米,收敛迭代次数减少33.3%,单位里程能耗降低18%。该研究为无人机智能化巡检提供了高精度、强适应性的解决方案,具有显著的工程应用价值。

Abstract:

Currently, UAV inspection has become an efficient and safe means of inspection, especially in the field of power lines, oil pipelines and other fields with a wide range of application prospects; in order to improve the efficiency and accuracy of the UAV inspection, reduce the inspection time and cost, the study designed a UAV inspection path planning system based on BeiDou satellite navigation, and the ant colony algorithm is improved by introducing obstacle exclusion weights and new heuristic factors. The system uses the Beidou satellite navigation module to achieve high-precision positioning, and combines the dynamic obstacle repulsion potential field model to effectively improve the obstacle avoidance ability of the UAV in a complex environment. In addition, the system design fully considers the endurance capability and flight safety of the UAV, and realizes the efficient inspection of the UAV in the limited power by optimizing the path planning strategy. In the experiments, in the simple environment, the optimal path length of the improved ant colony algorithm has an optimal path length of 186.54m in simple environment and 200.32m in complex environment, both of which are significantly better than the traditional ant colony algorithm"s 238.64m and 248.34m; At the same time, in the dynamic obstacle scene, the improved ant colony algorithm performs better in path optimization, and the planned path has higher safety, which can effectively avoid the dynamic obstacle, thus reducing the collision risk. The results show that the improved ACO algorithm can effectively improve the efficiency and accuracy of UAV inspection, and reduce the inspection time and cost; the study provides an efficient and reliable solution for UAV inspection path planning, which can improve the efficiency and safety of inspection work.