Kernekoncepter
The author proposes a gait-based coordinate optimization method to overcome the curse of dimensionality in geometric motion planning for high-dimensional systems.
Resumé
Geometric motion planning offers effective tools for locomotion analysis. The proposed method optimizes gaits in high-dimensional systems, showing better efficiency compared to reduced-order models. By combining coordinate optimization and local metrics, the approach takes a step towards geometric motion planning for complex systems.
Statistik
"We test our method in two classes of high-dimensional systems - low Reynolds number swimmers and free-falling Cassie - with up to 11-dimensional shape variables."
"The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model."
"For this reason, applying geometric motion planning to high-dimensional systems becomes nearly impractical."
"Although attempts have been made to address this issue via random sampling of the configuration space in a mesh-free manner, the number of samples required still increases exponentially with the dimensionality."
"In this paper, we take a step towards geometric motion planning for high-dimensional systems by proposing a new method for coordinate optimization and a unified modeling framework."
Citater
"The resulting optimal gait in the high-dimensional system shows better efficiency compared to that of the reduced-order model."
"By combining these two approaches, we take a step towards geometric motion planning for high-dimensional systems."
"Our proposed local metric representation approach will yield the same feasible projections of local connections as alternative modeling methods."
"The subspace formed by the gait captures a large portion of the maximum attainable CCF flux."
"The optimal gait covers a CCF-rich region in the subspace."