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.

翻译：本研究针对半自动化机器人拆卸操作中的多目标序列优化问题展开研究。为此，我们采用了一种受非支配排序遗传算法（NSGA）-III启发的多目标遗传算法（MaOGA），并结合基于目标产品三维计算机辅助设计（CAD）模型中存储的几何信息，通过几何与机器人仿真推导出的机器人拆卸导向约束及目标函数。该MaOGA算法并非随机生成初始染色体，而是基于接触与连接图（CCG）生成初始染色体集，以避免陷入局部最优并实现可重复的收敛性。优化过程对可行性、稳定性施加约束，并对难度、效率、优先级及可分配性等目标函数进行优化，从而在满足半自动化机器人拆卸强制性要求的前提下，生成符合多项优选条件的拆卸序列。受NSGA-III启发的MaOGA还利用非支配排序与参考线小生境技术，进一步促进稳定且稳健的探索，并均匀降低整体评估值。我们在复杂产品（36个零件）的序列生成实验中证明，所提方法能够以100%的成功率持续生成可行且稳定的序列，使多项优选条件更接近半自动化机器人拆卸操作所需的最优解。