  Autonomous navigation in highly populated areas remains a challenging task
for robots because of the difficulty in guaranteeing safe interactions with
pedestrians in unstructured situations. In this work, we present a crowd
navigation control framework that delivers continuous obstacle avoidance and
post-contact control evaluated on an autonomous personal mobility vehicle. We
propose evaluation metrics for accounting efficiency, controller response and
crowd interactions in natural crowds. We report the results of over 110 trials
in different crowd types: sparse, flows, and mixed traffic, with low- (< 0.15
ppsm), mid- (< 0.65 ppsm), and high- (< 1 ppsm) pedestrian densities. We
present comparative results between two low-level obstacle avoidance methods
and a baseline of shared control. Results show a 10% drop in relative time to
goal on the highest density tests, and no other efficiency metric decrease.
Moreover, autonomous navigation showed to be comparable to shared-control
navigation with a lower relative jerk and significantly higher fluency in
commands indicating high compatibility with the crowd. We conclude that the
reactive controller fulfils a necessary task of fast and continuous adaptation
to crowd navigation, and it should be coupled with high-level planners for
environmental and situational awareness.
