Configuration-space Bayesian optimisation for stress-critical pose identification on a hyper-redundant robot

Abstract

Abstract Serial manipulators can have strongly varying internal stresses within their configuration space, making the identification of stress-critical poses important for structural design.Because each candidate configuration requires a computationally expensive Finite Element Analysis (FEA), high-fidelity simulations quickly become impractical.This paper proposes a Bayesian optimisation framework that searches directly over the robot configuration space, using peak Von Mises stress as the objective.For each candidate configuration, statically consistent interface reactions are computed and applied as boundary conditions in the structural model.A Gaussian process surrogate with an ARD Matérn 5/2 kernel models the configuration-stress relationship, while the Bayesian algorithm with Expected Improvement acquisition function selects new configurations for parallel evaluation.A case study on a cable-driven hyper-redundant robot demonstrates that the proposed workflow can identify configurations with critical stress states and accurately map the high-stress region with a limited number of simulations.The results show that the framework can support both structural verification and the detection of potentially critical robot poses during operation.