State-Based Disassembly Planning

2025-01-09

AI Summary

In complex industrial assembly disassembly planning, high-fidelity physics simulation leads to low search efficiency, difficulty in modeling rotational motion, and severe state redundancy. To address these challenges, this paper proposes a novel planning paradigm based on intermediate motion state caching. Our key contributions are: (1) the first introduction of Directional Blocking Graphs (DBGs), which jointly encode geometric and state information to precisely characterize motion constraints; (2) two domain-specific heuristic evaluation functions that enhance search directionality and autonomy; and (3) a translation-prioritized physics-engine-driven modeling framework coupled with an incremental state caching mechanism, effectively mitigating state-space explosion. Extensive experiments on a benchmark of thousands of real-world industrial assemblies demonstrate that our method significantly outperforms state-of-the-art approaches in both disassembly success rate and computational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, the efficiency suffers when tackling intricate disassembly tasks that require numerous simulations and increased simulation time. In this work, we propose a State-Based Disassembly Planning (SBDP) approach, prioritizing physics-based simulation with translational motion over rotational motion to facilitate autonomy, reducing dependency on human input, while storing intermediate motion states to improve search scalability. We introduce two novel evaluation functions derived from new Directional Blocking Graphs (DBGs) enriched with state information to scale up the search. Our experiments show that SBDP with new evaluation functions and DBGs constraints outperforms the state-of-the-art in disassembly planning in terms of success rate and computational efficiency over benchmark datasets consisting of thousands of physically valid industrial assemblies.

Problem

• Complex Object Disassembly
• Physical Simulation Efficiency
• Computational Resource Consumption

Innovation

• SBDP
• Translation-based Physical Simulation
• Enhanced Directional Blocking Diagram