Planning and Execution of Dynamic Whole-Body Locomotion for a Hydraulic Quadruped on Challenging Terrain.
Winkler, A. W.; Mastalli, C.; Havoutis, I.; Focchi, M.; Caldwell, D.; and Semini, C.
In
IEEE International Conference on Robotics and Automation (ICRA).
2015
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@inproceedings{winkler15,
author = {Winkler, Alexander W. and
Mastalli, Carlos and
Havoutis, Ioannis and
Focchi, Michele and
Caldwell, Darwin and
Semini, Claudio},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
title = {Planning and Execution of Dynamic Whole-Body Locomotion
for a Hydraulic Quadruped on Challenging Terrain},
year = {2015},
pages = {5148-5154},
doi = {10.1109/ICRA.2015.7139916},
abstract = {We present a framework for dynamic
quadrupedal locomotion over challenging terrain, where
the choice of appropriate footholds is crucial for the success
of the behaviour. We build a model of the environment on-line
and on-board using an efficient occupancy grid representation.
We use Any-time-Repairing A* (ARA*) to search over a tree
of possible actions, choose a rough body path and select the
locally-best footholds accordingly. We run a n-step lookahead
optimization of the body trajectory using a dynamic stability
metric, the Zero Moment Point (ZMP), that generates natural
dynamic whole-body motions. A combination of floating-base
inverse dynamics and virtual model control accurately
executes the desired motions on an actively compliant system.
Experimental trials show that this framework allows us to
traverse terrains at nearly 6 times the speed of our previous
work, evaluated over the same set of trials.}
}
We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body motions. A combination of floating-base inverse dynamics and virtual model control accurately executes the desired motions on an actively compliant system. Experimental trials show that this framework allows us to traverse terrains at nearly 6 times the speed of our previous work, evaluated over the same set of trials.