论文标题

使用深层神经网络,用于提高无人机敏捷性的智能电源

Smart Power Supply for UAV Agility Enhancement Using Deep Neural Networks

论文作者

Liu, Yanze, Chen, Xuhui, Du, Yanhai, Liu, Rui

论文摘要

最近,无人驾驶汽车(UAV)已被广泛部署在各种现实世界中,例如灾难救援和包装交付。这些工作环境中的许多都是非结构化的,具有不确定和动态的障碍。无人机碰撞经常发生。具有高敏捷性的无人机非常希望调整其动作以适应这些环境动态。但是,无人机敏捷性受电池电量输出的限制。特别是,在需求随着环境和无人机条件而动态变化的同时,无人机的电源系统在运动计划中的实际功率需求不断变化。很难准确,及时将电源与运动策划中的电源需求保持一致。这种不匹配将导致对无人机的电源不足并导致运动延迟的调整,从而大大增加了与障碍物碰撞的风险,因此破坏了无人机的敏捷性。为了提高无人机敏捷性,开发了一种新颖的智能电源解决方案,即敏捷性增强电源(AEP),以便在适当的时机上积极准备适当的量功能,以增强敏捷性支持运动计划。此方法在物理电源系统和无人机计划之间建立了桥梁。随着敏捷性运动计划,将增强无人机在复杂工作环境中的安全性。为了评估AEP的有效性,采用了“社区安全巡逻任务”的任务。电源通过燃料电池,电池和电容器的混合整合来实现。 AEP在改善无人机敏捷性方面的有效性得到了成功,及时的电源,提高任务成功率和系统安全性以及降低任务持续时间的验证。

Recently unmanned aerial vehicles (UAV) have been widely deployed in various real-world scenarios such as disaster rescue and package delivery. Many of these working environments are unstructured with uncertain and dynamic obstacles. UAV collision frequently happens. An UAV with high agility is highly desired to adjust its motions to adapt to these environmental dynamics. However, UAV agility is restricted by its battery power output; particularly, an UAV's power system cannot be aware of its actual power need in motion planning while the need is dynamically changing as the environment and UAV condition vary. It is difficult to accurately and timely align the power supply with power needs in motion plannings. This mismatching will lead to an insufficient power supply to an UAV and cause delayed motion adjustments, largely increasing the risk of collisions with obstacles and therefore undermine UAV agility. To improve UAV agility, a novel intelligent power solution, Agility-Enhanced Power Supply (AEPS), was developed to proactively prepare appropriate amount powers at the right timing to support motion planning with enhanced agility. This method builds a bridge between the physical power system and UAV planning. With agility-enhanced motion planning, the safety of UAV in complex working environment will be enhanced. To evaluate AEPS effectiveness, missions of "patrol missions for community security" with unexpected obstacles were adopted; the power supply is realized by hybrid integration of fuel cell, battery, and capacitor. The effectiveness of AEPS in improving UAV agility was validated by the successful and timely power supply, improved task success rate and system safety, and reduced mission duration.

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