This work explores the challenges of creating a scalable and robust robot navigation system that can traverse both indoor and outdoor environments to reach distant goals. We propose a navigation system architecture called IntentionNet that employs a monolithic neural network as the low-level planner/controller, and uses a general interface that we call intentions to steer the controller. The paper proposes two types of intentions, Local Path and Environment (LPE) and Discretised Local Move (DLM), and shows that DLM is robust to significant metric positioning and mapping errors. The paper also presents Kilo-IntentionNet, an instance of the IntentionNet system using the DLM intention that is deployed on a Boston Dynamics Spot robot, and which successfully navigates through complex indoor and outdoor environments over distances of up to a kilometre with only noisy odometry.

翻译：本研究探讨了构建可扩展且鲁棒的机器人导航系统所面临的挑战，该系统需能在室内外环境中穿行以抵达远距离目标。我们提出了一种名为IntentionNet的导航系统架构，该架构采用单一神经网络作为底层规划器/控制器，并通过一种称为“意图”的通用接口来引导控制器。本文提出了两种意图类型：局部路径与环境（LPE）和离散化局部移动（DLM），并证明DLM对显著的度量定位与建图误差具有鲁棒性。本文还介绍了Kilo-IntentionNet——这是采用DLM意图的IntentionNet系统的一个实例，该系统部署于波士顿动力Spot机器人上，在仅使用含噪声里程计的条件下，成功在长达一公里的复杂室内外环境中实现了导航。