A Cyber-Physical framework for Energy-Aware distributed optimization of 6-DOF robotic manufacturing cells via hybrid bioinspired metaheuristics

Energy-aware coordination is a fundamental cornerstone for the future of autonomous Cyber-Physical Systems (CPS). This work proposes a novel distributed CPS framework to address the decoupling between high-level coordination of autonomous agents and the nonlinear dynamics of six-degree-of-freedom (6-DOF) manipulators. These dynamics are modeled through Euler-Lagrange equations and electromechanical dissipation heuristics. To discover energy-efficient coordination strategies, a hybrid algorithm based on Ant Colony Optimization (ACO), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO) was developed. The framework was rigorously validated through a three-phase experimental protocol, including 1,600 high-density scalability simulations with clusters of up to 20 agents, complemented by a computational complexity analysis. Results demonstrate that the ACO-driven component reduces median total energy by 55.4% relative to the PSO-only baseline in the stochastic analysis, while the integration of PSO and FA demonstrates robust empirical safety performance even in high-density environments. Such gains are achieved while upholding strict operational fidelity, with positional deviations constrained within a 3.5% threshold. The framework further exhibits statistically validated robustness across diverse multi-agent geometric configurations, supported by non-parametric hypothesis testing and tail-sensitive safety analyses. The overall findings demonstrate the effectiveness, scalability, and robustness of the framework for intelligent digital twins and the transition from Industry 4.0 to Industry 5.0.