Safe-Ballbot: Tilt-Consistent MPC with Control Barrier Functions for Ballbot Navigation
Overview
This research presents a two-layer navigation scheme for a three-omni-wheel ballbot.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · clearance maintained: 10 other · Basis not reported
“steady tilt that the barrier function admits, so that the plan never asks for a lean the tracker refuses. For obstacles whose motion is only predicted, the obstacles are inflated by conformal prediction radii that bound the prediction error with a prescribed probability. In MuJoCo, with omni-wheel roller contacts and torque saturation, the scheme keeps at least $10$\,cm of clearance, tracks the plan within $2$\,cm R”
Exact source · revision 1Source owner not reported
Reported quantity · tracking error: 2 other · Basis not reported
“steady tilt that the barrier function admits, so that the plan never asks for a lean the tracker refuses. For obstacles whose motion is only predicted, the obstacles are inflated by conformal prediction radii that bound the prediction error with a prescribed probability. In MuJoCo, with omni-wheel roller contacts and torque saturation, the scheme keeps at least $10$\,cm of clearance, tracks the plan within $2$\,cm R”
Exact source · revision 1Source owner not reported
Reported quantity · planning time: 9 other · Basis not reported · Differing source assertions
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
Reported quantity · planning time: 12 other · Basis not reported · Differing source assertions
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
Reported quantity · planning time: 10 other · Basis not reported · Differing source assertions
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
Reported quantity · planning time: 15 other · Basis not reported · Differing source assertions
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
Reported quantity · collision removal scenarios: 30 other · reported trials
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“MS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-bal”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSafe-Ballbot: Tilt-Consistent MPC with Control Barrier Functions for Ballbot Navigation
This research presents a two-layer navigation scheme for a three-omni-wheel ballbot.
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