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：连续环境中的视觉-语言导航任务（VLN-CE）旨在训练自主智能体通过视觉观测与语言指令，在三维连续环境中执行底层动作以实现导航。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工作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，严重时甚至被困于障碍区域而无法完成导航任务。针对上述问题，本文系统研究了VLN-CE中的各类碰撞场景，并提出一种分类方法来预测碰撞的潜在成因。进一步，我们提出名为Safe-VLN的新型VLN-CE算法以增强碰撞规避能力，该算法包含两个核心组件：航路点预测器与导航器。具体而言，航路点预测器利用模拟的2D激光雷达占据掩码，防止预测航路点落入障碍密集区域；导航器则采用"碰撞后重选"策略，避免机器人智能体陷入持续碰撞的循环。在R2R-CE数据集上的评估结果表明，Safe-VLN在提升导航性能的同时显著降低了碰撞事件的发生率。