Name
Abstract | ||
|---|---|---|
We control a biped robot moving in a crowd with a Model Predictive Control (MPC) scheme that generates stable walking motions, with automatic footstep placement. Most walking strategies propose to re-plan the walking motion to adapt to changing environments only once at every footstep. This is because a footstep is planted on the ground, it usually stays there at a constant position until the next footstep is initiated, what naturally constrains the capacity for the robot to react and adapt its motion in between footsteps. The objective of this paper is to measure if re-planning the walking motion more often than once at every footstep can lead to an improvement in collision avoidance when navigating in a crowd. Our result is that re-planning twice (or more) during each footstep leads to a significant reduction of the number of collisions when walking in a crowd, but depends on the density of the crowd. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968070 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | DocType | ISSN |
walking motion,planning period,MPC-based navigation,biped robot,model predictive control scheme,stable walking motions,automatic footstep placement,walking strategies | Conference | 2153-0858 |
ISBN | Citations | PageRank |
978-1-7281-4005-6 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Ciocca | 1 | 1 | 1.04 |
| Pierre-Brice Wieber | 2 | 3 | 2.12 |
| Th. Fraichard | 3 | 86 | 7.97 |