Many-Objective-Optimized Semi-Automated Robotic Disassembly Sequences
CoRR(2024)
摘要
This study tasckles the problem of many-objective sequence optimization for
semi-automated robotic disassembly operations. To this end, we employ a
many-objective genetic algorithm (MaOGA) algorithm inspired by the
Non-dominated Sorting Genetic Algorithm (NSGA)-III, along with
robotic-disassembly-oriented constraints and objective functions derived from
geometrical and robot simulations using 3-dimensional (3D) geometrical
information stored in a 3D Computer-Aided Design (CAD) model of the target
product. The MaOGA begins by generating a set of initial chromosomes based on a
contact and connection graph (CCG), rather than random chromosomes, to avoid
falling into a local minimum and yield repeatable convergence. The optimization
imposes constraints on feasibility and stability as well as objective functions
regarding difficulty, efficiency, prioritization, and allocability to generate
a sequence that satisfies many preferred conditions under mandatory
requirements for semi-automated robotic disassembly. The NSGA-III-inspired
MaOGA also utilizes non-dominated sorting and niching with reference lines to
further encourage steady and stable exploration and uniformly lower the overall
evaluation values. Our sequence generation experiments for a complex product
(36 parts) demonstrated that the proposed method can consistently produce
feasible and stable sequences with a 100
preferred conditions closer to the optimal solution required for semi-automated
robotic disassembly operations.
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