2014 IEEE International Conference on Robotics and Automation (ICRA) 2014
DOI: 10.1109/icra.2014.6907259
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Robust ladder-climbing with a humanoid robot with application to the DARPA Robotics Challenge

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Cited by 26 publications
(14 citation statements)
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“…However, their robot used two-feet-one-rung intermediary transitions before climbing each next rung, and climbed up backward to avoid collisions between knees and rungs when bending the knees. The HUBO+ humanoid robot, based on multi-contact planning technology [56], could also climb almost all of it, using a similar backward strategy [33] and a two-feet on one-rung transition phases, together with arm grasps on stringers. It failed at the last rung.…”
Section: Introductionmentioning
confidence: 99%
“…However, their robot used two-feet-one-rung intermediary transitions before climbing each next rung, and climbed up backward to avoid collisions between knees and rungs when bending the knees. The HUBO+ humanoid robot, based on multi-contact planning technology [56], could also climb almost all of it, using a similar backward strategy [33] and a two-feet on one-rung transition phases, together with arm grasps on stringers. It failed at the last rung.…”
Section: Introductionmentioning
confidence: 99%
“…However, we are planning to servo the robot with low PD gains and a feedforward term u = K p ǫ + K sǫ + D(q,q): K x being the gains, ǫ the servo position error and D the feedforward term. This idea was also discussed in [2], where the K p gain was adjusted in the gripper at the cost of loosing precision, whereas D was left for future work.…”
Section: Discussionmentioning
confidence: 99%
“…In brief, a multi-contact planner (MCP) uses the HRP-2, ladder and environment models (assumed known) to generate offline a sequence of postures in contact to climb the ladder. For the ladder, we use nearly the parametrization in [2], whereas the robot is covered by patches of spheres and tori for smooth distance computation [12].The postures computed by the MCP are passed to a finite state machine (FSM) that will split them into subtasks so as to account for uncertainties during task execution. The FSM elaborates additional tasks and changes on-line their objectives to deal with different primitives (contact adding, grasps, contact removal, center of mass shifts, etc).…”
Section: Multi-contact Plannermentioning
confidence: 99%
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