The task of vision-and-language navigation in continuous environments (VLN-CE) aims at training an autonomous agent to perform low-level actions to navigate through 3D continuous surroundings using visual observations and language instructions. The significant potential of VLN-CE for mobile robots has been demonstrated across a large number of studies. However, most existing works in VLN-CE focus primarily on transferring the standard discrete vision-and-language navigation (VLN) methods to continuous environments, overlooking the problem of collisions. Such oversight often results in the agent deviating from the planned path or, in severe instances, the agent being trapped in obstacle areas and failing the navigational task. To address the above-mentioned issues, this paper investigates various collision scenarios within VLN-CE and proposes a classification method to predicate the underlying causes of collisions. Furthermore, a new VLN-CE algorithm, named Safe-VLN, is proposed to bolster collision avoidance capabilities including two key components, i.e., a waypoint predictor and a navigator. In particular, the waypoint predictor leverages a simulated 2D LiDAR occupancy mask to prevent the predicted waypoints from being situated in obstacle-ridden areas. The navigator, on the other hand, employs the strategy of `re-selection after collision' to prevent the robot agent from becoming ensnared in a cycle of perpetual collisions. The proposed Safe-VLN is evaluated on the R2R-CE, the results of which demonstrate an enhanced navigational performance and a statistically significant reduction in collision incidences.

翻译：连续环境下的视觉语言导航任务(VLN-CE)旨在训练自主智能体通过视觉观测和语言指令执行低层级动作，以在连续三维环境中导航。VLN-CE在移动机器人领域的巨大潜力已通过大量研究得到验证。然而，现有VLN-CE研究主要聚焦于将标准离散视觉语言导航(VLN)方法迁移至连续环境，普遍忽视了碰撞问题。这种疏忽常导致智能体偏离规划路径，严重时甚至使智能体陷入障碍区域并导致导航任务失败。针对上述问题，本文系统研究了VLN-CE中的各类碰撞场景，提出了一种用于预测碰撞根本诱因的分类方法。进一步地，本文提出名为Safe-VLN的新型VLN-CE算法以增强碰撞避免能力，该算法包含两大核心组件：航点预测器和导航器。其中，航点预测器利用模拟的2D激光雷达占用掩码，确保预测航点不位于障碍密集区域；导航器则采用"碰撞后重选"策略，防止机器人智能体陷入连续碰撞的恶性循环。在R2R-CE数据集上的评估表明，所提Safe-VLN算法显著提升了导航性能，并实现了具有统计显著性的碰撞次数削减。