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UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU

机译:无人机群控制:使用GPU计算数字信息素字段

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摘要

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention, and common swarm control methods require expensive computational power. This paper begins with a discussion on how to improve upon current user interfaces and then reviews a swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. Next, a more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the graphics processing unit (GPU) in today\u27s graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU\u27s pipeline [1]. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this paper presents a vision of how to combine these new interface ideas and performance enhancements into an effective swarm control interface and training simulator.
机译:我们未来的军事力量将是复杂的:有人和无人部队的高度整合。这些无人部队可以单独或在一群人中起作用。未来的作战人员是否准备与这些新部队并肩作战并加以利用,取决于创建可用的界面和训练模拟器。困难在于当前的无人飞行器(UAV)控制界面需要太多的操作员注意,而普通的机群控制方法则需要昂贵的计算能力。本文首先讨论如何改进当前的用户界面,然后回顾一种群体控制方法,即数字信息素领域。此方法使用数字信息素将群内单个单元的移动偏向有吸引力的区域,并远离危险或没有吸引力的区域。接下来,介绍了一种用于执行信息素场计算的更有效方法,该方法通过将ADAPTIV群控制算法重塑为GPU管线可接受的形式来利用当今图形卡中图形处理单元(GPU)的功能。 [1]。 GPU ADAPTIV实现已在涉及多达50,000个虚拟UAV的场景中进行了测试。与同类CPU实施相比,GPU版本的执行速度是CPU版本的30倍以上。这种收益直接转化为降低培训今天的未来战士和派遣明日成群的费用。最后,本文提出了如何将这些新的界面思想和性能增强功能组合到有效的群体控制界面和训练模拟器中的愿景。

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