Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification
- URL: http://arxiv.org/abs/2605.21026v1
- Date: Wed, 20 May 2026 11:00:25 GMT
- Title: Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification
- Abstract summary: This study proposes an analytical framework that provides actionable guidance focused on fastener reduction.<n>The framework translates robotic disassembly sequence planning outcomes into component influence scores.<n>Experiments on seven household appliances demonstrate that the framework successfully targets redundant fasteners.
- Score: 6.078622137171499
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quantitative feedback to identify which structural elements hinder robotic operations. To address this, this study proposes an analytical framework that provides actionable redesign guidance focused on fastener reduction, as fasteners are numerous and ubiquitous components found in almost all manufactured products. Using a Computer-Aided Design (CAD) model and its automatically generated Contact-Connection-Constraint (CCC) graph, the framework translates robotic disassembly sequence planning outcomes into component influence scores. These scores reflect how often a component causes structural constraint violations or evaluation objective deteriorations in the robotic disassembly sequence. To visually highlight structural hindrances, the framework projects these scores onto the CAD geometry as 3D heatmaps. The system then analytically simulates the removal of highly influential fasteners. It reports the expected reductions in structural constraints, tool changes, and robot travel distances, while preventing structurally unsafe modifications by evaluating geometric stability metrics. Experiments on seven household appliances demonstrate that the framework successfully targets redundant fasteners. Removing the recommended fasteners simplified the structural dependencies by eliminating between 8 and 132 structural constraints on the graph depending on each product's structural configuration. Furthermore, it improved robotic operational efficiency by eliminating unnecessary tool change operations and shortening travel distances by 165 to 1675 millimeters wherever structurally permissible.
Related papers
- Static Timing Orchestration for Tree-Structured Robot Control Firmware [1.560130590380086]
We propose FineMote, a control firmware generation framework with a scheduling mechanism tailored for tree-structured device models.<n>We implement the proposed framework and evaluate it on real robotic control platforms.
arXiv Detail & Related papers (2026-08-05T09:04:54Z) - Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation [84.5255199507027]
Agentic Designer is a progressive framework that formulates structure-aware interior layout generation as an iterative and constraint-verified decision process.<n>By layout decomposing into modular stages of proposal, verification, synthesis and adjustment, the framework coordinates three specialized agents, a Generator, an Evaluator, and a Refiner.<n>This mechanism enforces stepwise geometric validation and correction before each placement is committed, thereby preventing error accumulation.
arXiv Detail & Related papers (2026-07-23T02:46:11Z) - COSMIC: Concurrent Optimization of Structure, Material, and Integrated Control for robotic systems [9.321828591115567]
Most robotic systems have their structure, materials, and locomotion designed separately.<n>This separation often leads to suboptimal designs, and we still have individual and collective contributions of these entities.<n>We propose a limited landscape-based co-design framework that simultaneously optimize material design, locomotion, and other complex autonomous behaviors.
arXiv Detail & Related papers (2026-05-12T19:00:01Z) - Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure [55.17771962316751]
Current 3D tokenizers treat representation as spatial compression, but leave component ownership and attachment validity implicit.<n>We formulate an alternative view, interface-centric generative states, in which tokenization constructs an operational state rather than a passive compressed code.<n>We instantiate this formulation with Component-Conditioned Canonical Local Tokens (C2LT-3D), factorizing representation into canonical local geometry, partition-conditioned context, and relational seam variables.
arXiv Detail & Related papers (2026-05-11T12:09:28Z) - Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
arXiv Detail & Related papers (2026-05-05T04:49:38Z) - System Design for Maintaining Internal State Consistency in Long-Horizon Robotic Tabletop Games [29.25150598992781]
Long-horizon tabletop games pose a distinct systems challenge for robotics.<n>Small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and derail interaction.<n>This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games.
arXiv Detail & Related papers (2026-03-26T12:54:29Z) - Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition [53.50448142467294]
RAIM is a multi-design and architecture-aware framework for repository-level feature addition.<n>It shifts away from linear patching by generating multiple diverse implementation designs.<n>Experiments on the NoCode-bench Verified dataset demonstrate that RAIM establishes a new state-of-the-art performance.
arXiv Detail & Related papers (2026-03-02T12:50:40Z) - From Perception to Action: An Interactive Benchmark for Vision Reasoning [51.11355591375073]
Causal Hierarchy of Actions and Interactions (CHAIN) benchmark designed to evaluate whether models can understand, plan, and execute structured action sequences grounded in physical constraints.<n> CHAIN shifts evaluation from passive perception to active problem solving, spanning tasks such as interlocking mechanical puzzles and 3D stacking and packing.<n>Our results show that top-performing models still struggle to internalize physical structure and causal constraints, often failing to produce reliable long-horizon plans and cannot robustly translate perceived structure into effective actions.
arXiv Detail & Related papers (2026-02-24T15:33:02Z) - Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction? [61.48804987263701]
We propose a suite of anatomy-aware evaluation metrics to assess structural completeness across anatomical structures.<n> CARE incorporates structural penalties during training to encourage anatomical preservation of significant structures.<n> CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels.
arXiv Detail & Related papers (2025-06-02T17:07:10Z) - Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage [12.889670445791985]
MIDAS is a "deploy-and-forget" approach for automated detection and localization of damage in structures.
It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder.
After learning from just 3 hours of data, MIDAS can autonomously detect and localize different types of unforeseen damage.
arXiv Detail & Related papers (2024-02-23T18:31:02Z) - RGL-NET: A Recurrent Graph Learning framework for Progressive Part
Assembly [30.143946636770025]
We tackle the problem of developing a generalized framework for assembly robust to structural variants.
Our network can learn more plausible predictions of shape structure by accounting for priorly assembled parts.
Our resulting latent space facilitates exciting applications such as shape recovery from the point-cloud components.
arXiv Detail & Related papers (2021-07-27T14:47:43Z) - Reducing Drift in Structure From Motion Using Extended Features [82.37566035407689]
Low-frequency long-range errors (drift) are an endemic problem in 3D structure from motion.
We present a method to dramatically reduce scale and positional drift by using extended structural features such as planes and vanishing points.
arXiv Detail & Related papers (2020-08-27T17:59:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.