E-Navi：面向资源受限平台的环境自适应导航
E-Navi: Environmental Adaptive Navigation for UAVs on Resource Constrained Platforms
AI总结：
E-Navi通过动态调整任务执行优化无人机在资源受限平台上的环境自适应导航性能
摘要：
自主导航系统对于无人机来说能够适应变化的环境至关重要。然而，现有的导航系统采用固定执行配置，没有考虑基于可用计算资源的环境动态，例如高执行频率和任务负载。这种静态方法导致了僵化的飞行策略和过度的计算，最终会降低飞行性能甚至导致无人机故障。尽管需要一个自适应系统，但动态调整工作负载仍然具有挑战性，因为量化环境复杂性和建模环境与系统配置之间关系存在困难。针对动态环境，本文提出E-Navi，一种面向无人机的环境自适应导航系统，该系统根据可用计算资源动态调整CPU上的任务执行。具体来说，通过动态调整地图分辨率和执行频率，重新设计无人机导航系统的感知-规划流水线，由定量的环境复杂性评估驱动。此外，E-Navi支持在具有不同计算能力的硬件平台上灵活部署。广泛的硬件在环和现实世界实验表明，所提出的系统在各种硬件平台上均显著优于基线方法，实现了高达53.9%的导航任务负载减少，高达63.8%的飞行时间节省，并提供了更稳定的速度控制。
英文摘要：
The ability to adapt to changing environments is crucial for the autonomous navigation systems of Unmanned Aerial Vehicles (UAVs). However, existing navigation systems adopt fixed execution configurations without considering environmental dynamics based on available computing resources, e.g., with a high execution frequency and task workload. This static approach causes rigid flight strategies and excessive computations, ultimately degrading flight performance or even leading to failures in UAVs. Despite the necessity for an adaptive system, dynamically adjusting workloads remains challenging, due to difficulties in quantifying environmental complexity and modeling the relationship between environment and system configuration. Aiming at adapting to dynamic environments, this paper proposes E-Navi, an environmental-adaptive navigation system for UAVs that dynamically adjusts task executions on the CPUs in response to environmental changes based on available computational resources. Specifically, the perception-planning pipeline of UAVs navigation system is redesigned through dynamic adaptation of mapping resolution and execution frequency, driven by the quantitative environmental complexity evaluations. In addition, E-Navi supports flexible deployment across hardware platforms with varying levels of computing capability. Extensive Hardware-In-the-Loop and real-world experiments demonstrate that the proposed system significantly outperforms the baseline method across various hardware platforms, achieving up to 53.9% navigation task workload reduction, up to 63.8% flight time savings, and delivering more stable velocity control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
