Enhancing efficiency and accuracy in robotic assembly task planning through tool integration using a hybrid class topper optimisation algorithm

Abstract

Uncertainties in robotic assembly can substantially influence the quality of assembly task planning, often resulting in suboptimal solutions. It is crucial to account for these uncertainties when developing assembly task plans that are both efficient and practical for multi-part products. To address such issues, the proposed method integrates the NelderMead simplex algorithm with the Class Topper Optimisation Algorithm to create a hybrid NelderMead Class Topper Optimisation Algorithm. This study uses a vibration generator as an example to illustrate the application of the proposed method. Ensuring tool accessibility is emphasised, and the assembly tasks are initialised accordingly. The feasibility of these tasks is determined using liaison and tool-integrated geometric feasibility predicate analysis. Multiple criteria are considered to achieve the most efficient robotic assembly task planning, including part reorientation, gripper or tool change and the energy required to assemble the part. The effectiveness and robustness of the proposed optimisation algorithm are demonstrated by comparing it with other algorithms, such as the teaching-learning-based algorithm, the genetic algorithm, the bees algorithm and the particle swarm optimisation algorithm. The results have shown that the proposed approach is highly effective for real-industrial relevant problems.