EANS：利用环境自适应导航策略降低无人机能耗
EANS: Reducing Energy Consumption for UAV with an Environmental Adaptive Navigation Strategy
AI总结：
本文提出EANS方法，通过分析无人机动态特性和自主导航流水线时间特性来动态调整导航策略，克服了流水线依赖等挑战，经硬件在环仿真和真实实验验证，显著提升了任务时间与能耗效率。
摘要：
无人机（UAVS）受限于机载能源。导航策略的优化通过调整UAVS流水线中的关键参数，直接影响飞行速度和轨迹，从而降低能耗。然而，现有技术在动态场景中往往采用静态且保守的策略，导致降能效率低下。动态调整导航策略需要克服任务流水线相互依赖、环境与策略的相关性以及参数选择等挑战。为解决上述问题，本文提出一种方法，通过分析UAVS的动态特性及自主导航流水线的时间特性来动态调整其导航策略，从而在响应环境变化时降低UAVS能耗。我们通过硬件在环（HIL）仿真和真实世界实验将该方法与基线进行对比，结果表明，该方法在任务时间上分别实现了3.2倍和2.6倍的提升，在能耗上分别实现了2.4倍和1.6倍的提升。
英文摘要：
Unmanned Aerial Vehicles (UAVS) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the UAVS pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the UAVS by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing UAVS energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3.2X and 2.6X improvements in mission time, 2.4X and 1.6X improvements in energy, respectively.
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