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This research introduces Backward Layout Search (BLS), a novel algorithmic approach for robotic assembly planning that optimizes part placement by working in reverse order of the assembly sequence. By leveraging geometric and kinematic constraints to filter feasible initial poses, BLS significantly reduces computational search time while guaranteeing collision-free execution compared to traditional forward-search methods. The work advances machine learning-adjacent robotics optimization, offering a more efficient framework for automating complex manufacturing tasks where workspace dynamics change after each step.
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Backward Layout Search for Sequence-Constrained Robotic Assembly

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arXiv:2608.18454v1 Announce Type: new Abstract: Robotic assembly layout planning must determine the assembly site and the initial pose of each part while ensuring collision-free execution of a prescribed assembly sequence. This problem is challenging because the obstacle environment changes after each assembly step, and unassembled parts re maining in the workspace may block robot motions. We observe that the feasibility of each assembly step depends only on the initial poses of the current and later-assembled parts. Based on this dependency, we propose Backward Layout Search (BLS), which assigns initial part poses in reverse assembly order. Each expansion performs geometric, kinematic, grasp, and prescribed-motion checks, while collision masks and candidate set filtering remove infeasible initial part pose candidates. Promising partial layouts are retained through beam selection, and complete layouts are validated by full motion planning in forward assembly order. Experiments on five assembly models show that BLS produces collision-free executable layouts and reduces step evaluations and search time compared with a matched forward search.

Abstract:Robotic assembly layout planning must determine the assembly site and the initial pose of each part while ensuring collision-free execution of a prescribed assembly sequence. This problem is challenging because the obstacle environment changes after each assembly step, and unassembled parts re maining in the workspace may block robot motions. We observe that the feasibility of each assembly step depends only on the initial poses of the current and later-assembled parts. Based on this dependency, we propose Backward Layout Search (BLS), which assigns initial part poses in reverse assembly order. Each expansion performs geometric, kinematic, grasp, and prescribed-motion checks, while collision masks and candidate set filtering remove infeasible initial part pose candidates. Promising partial layouts are retained through beam selection, and complete layouts are validated by full motion planning in forward assembly order. Experiments on five assembly models show that BLS produces collision-free executable layouts and reduces step evaluations and search time compared with a matched forward search.

Robotics (cs.RO)

arXiv:2608.18454 [cs.RO]

(or arXiv:2608.18454v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.18454 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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