🤖 AI Summary
This study addresses the kinematic structure design optimization of robotic hands by formulating it as a combinatorial optimization problem, aiming to achieve high-performance configurations under constraints on finger selection and structural feasibility. The work proposes a unified modeling framework based on Quadratic Unconstrained Binary Optimization (QUBO), which, for the first time, systematically encodes design criteria—such as workspace overlap, mutually exclusive choices, and structural dependencies—into a quadratic objective function amenable to direct solution via quantum annealing or classical simulated annealing. An experimental instance with 27 binary variables yields feasible designs, with objective values converging as the number of samples increases, thereby demonstrating the framework’s feasibility, effectiveness, and potential generalizability to other robotic systems.
📝 Abstract
This paper presents a quadratic unconstrained binary optimization-based formulation framework for robot design optimization using kinematic structure-level evaluation metrics. In the proposed framework, classical computation is used to evaluate design-dependent metrics while the resulting combinatorial selection problem is formulated in a structure compatible with quantum annealing-based optimization. A robotic hand is adopted as a representative case study, as its performance is determined by both the individual kinematic characteristics of each finger and interaction terms. The proposed formulation incorporates individual design rewards, overlap workspace interactions, one-hot constraint, and structural dependency penalties into a unified quadratic model. A 27-variable robotic hand design problem is constructed, and simulated annealing is used as a classical baseline to verify the feasibility of the formulation. Quantum annealing is further performed to examine the applicability of the proposed formulation to annealing-based hardware execution. The results show that feasible design combinations satisfying both one-hot selection and pairwise constraints can be obtained, with the observed objective-value range becoming narrower as the number of reads increases. In addition, the formulation process is discussed for other robotic systems. The proposed framework provides a generalized approach for transforming kinematic structure-based robot design problems into combinatorial optimization problems.