Geodesic Path Planning on Configuration Space Manifolds: Shorter, Smoother Trajectories for Humanoid Robots

We demonstrate that replacing Euclidean distance with geodesic distance on the toroidal configuration space of revolute-joint robots produces 26-29% shorter paths across all tested morphologies, from 6-DOF industrial arms to 56-DOF humanoid robots (p < 10^-20). Tested on Boston Dynamics Atlas, Tesla Optimus, and industrial arms. No hardware or algorithm changes required. Patent pending (AU 2026902297).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-22
DOI
https://doi.org/10.5281/zenodo.19159107
Primary Topic
Robotic Locomotion and Control
Type
article
Field-Weighted Citation Impact
0.00
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article

Geodesic Path Planning on Configuration Space Manifolds: Shorter, Smoother Trajectories for Humanoid Robots

Nicholas Muir
Zenodo (CERN European Organization for Nuclear Research)
Robotic Locomotion and Control
article

Geodesic Path Planning on Configuration Space Manifolds: Shorter, Smoother Trajectories for Humanoid Robots

Nicholas Muir
article en

Abstract

We demonstrate that replacing Euclidean distance with geodesic distance on the toroidal configuration space of revolute-joint robots produces 26-29% shorter paths across all tested morphologies, from 6-DOF industrial arms to 56-DOF humanoid robots (p < 10^-20). Tested on Boston Dynamics Atlas, Tesla Optimus, and industrial arms. No hardware or algorithm changes required. Patent pending (AU 2026902297).

Zenodo (CERN European Organization for Nuclear Research)
Robotic Research (United States) (US), Robotic Technology (United States) (US)
Sustainable cities and communities
Openalex Percentile: Top 13%
Robotic Locomotion and Control
DOIOpen Access PDFOpenAlex
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