  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.
