Power usage reduction of humanoid standing process using Q-Learning
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2016-01-29
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Abstract
An important area of research in humanoid robots is energy
consumption, as it limits autonomy, and can harm task performance.
This work focuses on power aware motion planning. Its principal aim is
to find joint trajectories to allow for a humanoid robot to go from crouch
to stand position while minimizing power consumption. Q-Learning (QL)
is used to search for optimal joint paths subject to angular position
and torque restrictions. A planar model of the humanoid is used, which
interacts with QL during a simulated offline learning phase. The best
joint trajectories found during learning are then executed by a physical
humanoid robot, the Aldebaran NAO. Position, velocity, acceleration,
and current of the humanoid system are measured to evaluate energy,
mechanical power, and Center of Mass (CoM) in order to estimate the
performance of the new trajectory which yield a considerable reduction
in power consumption.
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Atribución-NoComercial-CompartirIgual 2.5 Colombia