Small-scale autonomous airborne vehicles, such as micro-drones, are expected to be a central component of a broad spectrum of applications ranging from exploration to surveillance and delivery. This class of vehicles is characterized by severe constraints in computing power and energy reservoir, which impairs their ability to support the complex state-of-the-art neural models needed for autonomous operations. The main contribution of this paper is a new class of neural navigation models -- NaviSlim -- capable of adapting the amount of resources spent on computing and sensing in response to the current context (i.e., difficulty of the environment, current trajectory, and navigation goals). Specifically, NaviSlim is designed as a gated slimmable neural network architecture that, different from existing slimmable networks, can dynamically select a slimming factor to autonomously scale model complexity, which consequently optimizes execution time and energy consumption. Moreover, different from existing sensor fusion approaches, NaviSlim can dynamically select power levels of onboard sensors to autonomously reduce power and time spent during sensor acquisition, without the need to switch between different neural networks. By means of extensive training and testing on the robust simulation environment Microsoft AirSim, we evaluate our NaviSlim models on scenarios with varying difficulty and a test set that showed a dynamic reduced model complexity on average between 57-92%, and between 61-80% sensor utilization, as compared to static neural networks designed to match computing and sensing of that required by the most difficult scenario.

本文主要介绍了一种名为NaviSlim的新型神经导航模型，它是一种有能力根据当前环境、当前轨迹和导航目标选择计算资源和传感资源的神经网络架构，可以自动缩放模型复杂性，优化执行时间和能量消耗，并且可以自动降低传感器的功耗和采集时间，相较于静态神经网络，NaviSlim模型在不同难度的场景中平均可以降低57-92%的模型复杂性和61-80%的传感器利用率。

NaviSlim：自适应上下文感知导航和传感技术通过动态的可裁剪网络