Secure Cyber-Physical Robotic Automation for Energy-Efficient Industry 5.0

Abstract Advanced manufacturing systems increasingly require robotic automation that synchronize production processes, while considering energy consumption and cyber-physical security. This study presents an integrated optimized robotic process automation and business process management (ORPABPM) framework for secure and energy-efficient Industry 5.0 manufacturing. The framework connects business-process modelling and task orchestration with physical robot control, industrial monitoring, adaptive learning, and execution feedback. Its energy-efficient automation for robotics (EEAR) algorithm identifies energy-intensive operations and optimizes task scheduling and resource allocation, while the robotic adversarial threat detection (RATD) algorithm monitors sensory inputs and planning behavior to detect and mitigate blind-spot and planner attacks. The framework was evaluated on a hybrid hardware-in-the-loop manufacturing testbed integrating a programmable logic controller, CNC machinery, an industrial Internet of Things adaptor, Rockwell Automation RSEmulate, SimKit, and MATLAB. Experiments covered workloads of up to 72,000 tasks and assessed execution accuracy, energy consumption, throughput efficiency, and attack-induced error. The complete framework achieved 99.3% task-execution accuracy, completed up to 46,000 tasks while consuming 135 kWh, and maintained approximately 95% throughput efficiency at 40,000 completed tasks. Under blind-spot and planner attacks, the corresponding error rates were limited to 0.035% and 0.053%, respectively. These results indicate that jointly integrating process intelligence, energy-aware scheduling, and adversarial-threat detection can improve the operational efficiency and resilience of cyber-physical robotic manufacturing systems.