A self-evolving system for robotic disassembly sequence planning under uncertain interference conditions

Robotic disassembly sequence planning (DSP) is a research area that looks at the sequence of actions in the disassembly intending to achieve autonomous disassembly with high efficiency and low cost in remanufacturing and recycling applications. A piece of key input information being factored in DSP is the interference condition of a product, i.e., a mathematical representation of the spatial location of components in an assembly, usually in the form of a matrix. An observed challenge in the area is that the interference condition can be uncertain due to variations in the end-of-life conditions, and there is a lack of tools available in DSP under uncertain interference. To address this challenge, this paper proposes a new DSP method that can cope with uncertain interference conditions enabled by the fuzzification of DSP (FDSP). This new approach in the core is a fuzzy and dynamic modeling method in combination with an iterative re-planning strategy, and FDSP offers the capability for DSP to adapt to failures and self-evolve online. Three products are given to demonstrate FDSP.