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The generation of energy-efficient and dynamic-aware robot motions that
satisfy constraints such as joint limits, self-collisions, and collisions with
the environment remains a challenge. In this context, Riemannian geometry
offers promising solutions by identifying robot motions with geodesics on the
so-called configuration space manifold. While this manifold naturally considers
the intrinsic robot dynamics, constraints such as joint limits,
self-collisions, and collisions with the environment remain overlooked. In this
paper, we propose a modification of the Riemannian metric of the configuration
space manifold allowing for the generation of robot motions as geodesics that
efficiently avoid given regions. We introduce a class of Riemannian metrics
based on barrier functions that guarantee strict region avoidance by
systematically generating accelerations away from no-go regions in joint and
task space. We evaluate the proposed Riemannian metric to generate
energy-efficient, dynamic-aware, and collision-free motions of a humanoid robot
as geodesics and sequences thereof.
