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Scaling Sampling-based Motion Planning to Humanoid Robots
[article]
2016
arXiv
pre-print
Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling--based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no
arXiv:1607.07470v2
fatcat:moijwejdbjandhjj664qgxk3fm