State-Based Disassembly Planning

TL;DR

This paper introduces a State-Based Disassembly Planning method that improves efficiency and success rates in complex disassembly tasks by leveraging physics simulation, directional blocking graphs, and state storage to enhance search scalability.

Contribution

It proposes a novel SBDP approach that prioritizes translational motion, uses enriched directional blocking graphs, and introduces new evaluation functions to outperform existing methods.

Findings

Outperforms state-of-the-art in success rate and efficiency.

Effectively handles thousands of complex industrial assemblies.

Reduces simulation dependency and improves scalability.

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.