BOMO-RNN: a novel neural network controller for industrial robots with experimental validation

This paper introduces the Beetle Olfactory-based Manipulability Optimizer Recurrent Neural Network (BOMO-RNN), an advanced RNN-based controller designed to enhance the manipulability of redundantly actuated industrial robotic arms. The manipulability index, which quantifies the maneuverability of the robotic arm, is crucial for avoiding kinematic singularities that restrict the mobility of robotic arm in the task space. The proposed approach formulates an optimisation problem using the penalty method to incorporate the manipulability index into the tracking control objective function. Unlike conventional approaches that rely on velocity-level control and require precise initialisation, BOMO-RNN operates at the position level, allowing direct trajectory tracking from arbitrary starting configurations, thereby increasing flexibility and ease of deployment. This function aims to maximise maneuverability while ensuring accurate tracking of the reference trajectory, effectively avoiding joint-space singularities. The BOMO-RNN framework leverages a metaheuristic optimisation strategy, enabling efficient exploration of high-dimensional search spaces without requiring explicit Jacobian pseudo-inversion, significantly reducing computational overhead and improving numerical stability. The BOMO-RNN algorithm efficiently addresses the time-varying optimisation problem at the position level, eliminating the need for computationally intensive Jacobian pseudo-inversion. This ensures robustness in real-world scenarios where high-speed control and adaptability to dynamic environments are critical. The algorithm's convergence is theoretically analysed, and its performance is validated through numerical simulations and experimental results using the LBR IIWA 7-DOF robot. Extensive experimental verification demonstrates the effectiveness of BOMO-RNN across diverse trajectory patterns, including circular, sinusoidal, and piecewise straight-line motions, confirming its generalizability and practical applicability. The results demonstrate BOMO-RNN's practical effectiveness in optimising manipulability and its potential for real-world robotic applications.