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ARXIV:2604.01614 · ROBOTICS MOTION PLANNING · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01614ROBOTICS MOTION PLANNINGSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEAref Amiri · Steven M. LaValle · arXiv
A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time.
Opportunity summary
Pain A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control effort.
Feedback motion planning over cell decompositions provides a robust method for generating collision-free robot motion with formal guarantees. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Simulations demonstrate that our method generates measurably more direct paths, reducing total bending by an average of 91.40\% and LQR control effort by an…
Robotics Motion Planning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time.
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10.48550/arXiv.2604.01614A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time.
Abstract
Feedback motion planning over cell decompositions provides a robust method for generating collision-free robot motion with formal guarantees. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control effort. This paper presents a computationally efficient method to mitigate this issue for a given simplicial decomposition. A heuristic is introduced that systematically aligns and assigns local vector fields to produce more direct trajectories, complemented by a novel geometric algorithm that constructs a maximal star-shaped chain of simplexes around the goal. This creates a large ``funnel'' in which an optimal, direct-to-goal control law can be safely applied. Simulations demonstrate that our method generates measurably more direct paths, reducing total bending by an average of 91.40\% and LQR control effort by an average of 45.47\%. Furthermore, comparative analysis against sampling-based and optimization-based planners confirms the time efficacy and robustness of our approach. While the proposed algorithms work over any finite-dimensional simplicial complex embedded in the collision-free subset of the configuration space, the practical application focuses on low-dimensional ($d\le3$) configuration spaces, where simplicial decomposition is computationally tractable.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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PROBLEM
A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control effort.
METHOD
Feedback motion planning over cell decompositions provides a robust method for generating collision-free robot motion with formal guarantees. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control effort.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Simulations demonstrate that our method generates measurably more direct paths, reducing total bending by an average of 91.40\% and LQR control effort by an average of 45.47\%. Code availability is flagge...
WHY NOW
Robotics Motion Planning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
reducing total bending by an average of 91.40%
Explicitly stated numeric result in abstract with specific percentage
partial
LQR control effort by an average of 45.47%
Explicitly stated numeric result in abstract with specific percentage
partial
a novel geometric algorithm that constructs a maximal star-shaped chain of simplexes around the goal
Directly stated as a novel contribution in the abstract
partial
presents a computationally efficient method to mitigate this issue for a given simplicial decomposition
Directly stated in abstract but without specific timing measurements
partial
the practical application focuses on low-dimensional (d≤3) configuration spaces, where simplicial decomposition is computationally tractable
Explicit limitation stated in abstract with dimensional constraint
partial
our method generates measurably more direct paths
Directly stated in abstract with supporting evidence from bending reduction metrics
partial
comparative analysis against sampling-based and optimization-based planners confirms the time efficacy and robustness of our approach
Directly stated in abstract but without specific comparative metrics provided
partial
the proposed algorithms work over any finite-dimensional simplicial complex embedded in the collision-free subset of the configuration space
Explicit technical statement about algorithm generality in abstract
partial
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A computationally efficient method to generate smoother, less-bending robot motion paths, reducing control effort and planning time.
Segment
Robotics Motion Planning
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Commercial read
7.0/10 public viability
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