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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU
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Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU

机译：使用GPU的固定翼军用无人机快速遗传算法路径规划器

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摘要

Military unmanned aerial vehicles (UAVs) are employed in highly dynamic environments and must often adjust their trajectories based on the evolving situation. To operate autonomously and safely, a UAV must be equipped with a path planning module capable of quickly recalculating a feasible and quasi-optimal path in flight while in the event a new obstacle or threat has been detected or simply if the destination point is changed during the mission. To allow for a fast path planning, this paper proposes a parallel implementation of the genetic algorithm on graphics processing unit (GPU). The trajectories are built as series of line segments connected by circular arcs resulting in smooth paths suitable for fixed-wing UAVs. The fitness function we defined takes into account the dynamic constraints of the UAVs and aims to minimize fuel consumption and average flying altitude in order to improve range and avoid detection by enemy radars. This fitness function is also implemented on the GPU and different parallelization strategies were developed and tested for each step of the fitness evaluation. By exploiting the massively parallel architecture of GPUs, the execution time of the proposed path planner was reduced by a factor of 290x compared to a sequential execution on CPU. The path planning module developed was tested using 18 scenarios on six realistic three-dimensional terrains with multiple no-fly zones. We found that the proposed GPU-based path planner was able to find quasi-optimal solutions in a timely fashion allowing in-flight planning.
机译：军用无人机在高度动态的环境中使用，必须经常根据不断变化的情况调整其航迹。为了自主和安全地运行，无人机必须配备路径规划模块，该模块能够快速重新计算飞行中的可行和准最佳路径，而一旦检测到新的障碍物或威胁，或者仅仅是在转换过程中改变了目的地，使命。为了进行快速路径规划，本文提出了遗传算法在图形处理单元（GPU）上的并行实现。轨迹被构建为由圆弧连接的一系列线段，从而形成了适合固定翼无人机的平滑路径。我们定义的适应度功能考虑了无人机的动态约束，旨在最大程度地减少油耗和平均飞行高度，以提高射程并避免被敌方雷达探测到。该适应度功能也已在GPU上实现，并且针对适应度评估的每个步骤开发并测试了不同的并行化策略。通过利用GPU的大规模并行体系结构，与在CPU上顺序执行相比，拟议的路径规划器的执行时间减少了290倍。开发的路径规划模块在18个场景中在六个具有多个禁飞区的逼真的三维地形上进行了测试。我们发现，提出的基于GPU的路径规划器能够及时找到准最佳解决方案，从而可以进行飞行中的规划。

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