Many-Objective-Optimized Semi-Automated Robotic Disassembly Sequences

TL;DR

This paper presents a many-objective genetic algorithm tailored for optimizing robotic disassembly sequences, integrating CAD-based geometric data and constraints to generate feasible, stable, and efficient disassembly plans.

Contribution

It introduces a novel MaOGA approach inspired by NSGA-III, using contact and connection graphs for initial solutions and incorporating multiple constraints for improved sequence optimization.

Findings

Achieved 100% success rate in generating feasible disassembly sequences for complex products.

Produced sequences that satisfy multiple constraints and preferences effectively.

Demonstrated the method's robustness and consistency in complex disassembly scenarios.

Abstract

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% success rate, bringing the multiple preferred conditions closer to the optimal solution required for semi-automated robotic disassembly operations.