Dense Crowd Flow-Informed Path Planning
- URL: http://arxiv.org/abs/2206.00705v1
- Date: Wed, 1 Jun 2022 18:40:57 GMT
- Title: Dense Crowd Flow-Informed Path Planning
- Authors: Emily Pruc, Shlomo Zilberstein, and Joydeep Biswas
- Abstract summary: Flow-field extraction and discrete search are used to create Flow-Informed path planning.
A robot using FIPP was able not only to reach its goal more quickly but also was shown to be more socially compliant than a robot using traditional techniques.
- Score: 24.849908664615104
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Both pedestrian and robot comfort are of the highest priority whenever a
robot is placed in an environment containing human beings. In the case of
pedestrian-unaware mobile robots this desire for safety leads to the freezing
robot problem, where a robot confronted with a large dynamic group of obstacles
(such as a crowd of pedestrians) would determine all forward navigation unsafe
causing the robot to stop in place. In order to navigate in a socially
compliant manner while avoiding the freezing robot problem we are interested in
understanding the flow of pedestrians in crowded scenarios. By treating the
pedestrians in the crowd as particles moved along by the crowd itself we can
model the system as a time dependent flow field. From this flow field we can
extract different flow segments that reflect the motion patterns emerging from
the crowd. These motion patterns can then be accounted for during the control
and navigation of a mobile robot allowing it to move safely within the flow of
the crowd to reach a desired location within or beyond the flow.
We combine flow-field extraction with a discrete heuristic search to create
Flow-Informed path planning (FIPP). We provide empirical results showing that
when compared against a trajectory-rollout local path planner, a robot using
FIPP was able not only to reach its goal more quickly but also was shown to be
more socially compliant than a robot using traditional techniques both in
simulation and on real robots.
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