Disassembly sequence planning identifies physically viable plans to disassemble an assembly of parts. It is often used in end-of-life product design to verify the future ability to disassemble the product for recycling or repairs. This field is crucial towards a more automated product design and manufacturing process.

Our initial work used sampling-based motion planning techniques to find valid disassembly sequences by treating each part as a separate movable object or robot. It biased sampling along part face normals and was successful in disassembling several small puzzle-like problems.

Blocked Planar Puzzle

We have more recently developed a general framework for disassembly sequence planning. This framework is capable of allowing different types of search schemes (exhaustive vs. preemptive), various part separation techniques, and the ability to group parts, or not, into subassemblies to improve the solution efficiency and parallelism. This gives the new ability to approach the disassembly sequence planning problem in a truly hierarchical way. We also developed a method for subassembly identification based on collision information.

We use motion planning to generate disassembly sequences. Our approach treats the parts in the assembly as robots and operates in the composite configuration space of all the individual parts. It then searches in this composite configuration space for a valid disassembly sequence using popular sampling-based motion planning techniques.

Although a purely randomized approach to sampling is successful in systems with a small number of parts, typical assemblies consist of numerous parts and the corresponding composite C-spaces have high dimensionality. In addition, a completely randomized sampling approach would be ineffective since many important configurations for the disassembly sequence will involve closely packed parts, i.e., the disassembly sequence will pass through narrow passages in the C-space. Our solution to this problem is to bias the sampling by computing potential movement directions based on the geometric characteristics of configurations known to be reachable from the assembled configuration, such as the face normals of the parts.

Naive Part Swapping (unbiased sampling, blocks smaller for solvability)

Intelligent Part Swapping (sampling biased along face normals)

Our experimental results with several non-trivial puzzle-like assemblies show the potential of this approach:

Planar Puzzle

Blocked Planar Puzzle

3D Puzzle (10 parts)

Pentomino(12 parts)

We develop a new general framework for disassembly sequence planning. This framework is a flexible method for the complete disassembly of an object; versatile in its nature, allowing different types of search schemes (exhaustive vs. preemptive), various part separation techniques, and the ability to group parts, or not, into subassemblies to improve the solution efficiency and parallelism. This gives the new ability to approach the disassembly sequence planning problem in a truly hierarchical way.

By simply changing the definitions of the framework’s subroutines, a wide spectrum of disassembly search strategies may be achieved. We demonstrate two different search strategies using the framework that can either yield a single solution quickly or provide a spectrum of solutions from which an optimal may be selected.

We also develop a method for subassembly identification based on collision information. Our results show improved performance over an iterative motion planning based method for finding a single solution and greater functionality through hierarchical planning and optimal solution search.

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