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<p>To autonomously control a robot’s movement in complex and unpredictable environments, adaptive models must be developed that can continuously learn about and refine their future physical dynamics. This paper introduces a Self-Organising Physics Discovered Neural Architecture (SOPDNA) that unites Physics-Informed Neural Networks (PINNs) with Robotics Process Automation (RPA) and Digital Twin DT technologies to create an entirely automated, robust robotic motion control system. In contrast to traditional robotic motion control systems that rely on predefined mathematical models or offline training methods, SOPDNA automatically discovers previously unknown physical relationships between robotic motion components and continuously updates the controller policy during operation. The Digital Twin software acts as a nearly identical simulation environment to enable extensive self-experimentation and safety verification of SOPDNA while the RPA software automates all aspects of the SOPDNA learning process, including acquiring experimental data, detecting anomalies, retraining the learning model, and deploying optimised controllers, without requiring any human oversight. The core of SOPDNA consists of a PINN that incorporates a comprehensive mathematical representation of the robot’s physical constraints and uses symbolic regression techniques to estimate unmodelled dynamic variables, such as variable friction coefficients and environmental disturbances. Experimental validation using a robotic manipulator performing trajectory tracking and obstacle avoidance under changing payloads shows the proposed approach’s capabilities. The SOPDNA framework provides an overall trajectory-tracking accuracy of 98.4%, a 22.7% reduction in energy usage, and a 30.9% improvement in disturbance rejection when compared to traditional deep reinforcement learning (DRL) and Proportional-Integral-Derivative (PID)-based control systems. Results from the fault-injection experiments show SOPDNA’s ability to recover from degradation of actuators 41.3% faster than conventional methods. Overall, these findings support the potential for self-organising physics-discovered neural network architectures to enhance the development of robust, adaptive, intelligent robotic motion control systems capable of autonomously executing complex tasks.</p>

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A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Daksh Singla,
  • G. Logeswari,
  • K. Tamilarasi,
  • J. Deepika Roselind

摘要

To autonomously control a robot’s movement in complex and unpredictable environments, adaptive models must be developed that can continuously learn about and refine their future physical dynamics. This paper introduces a Self-Organising Physics Discovered Neural Architecture (SOPDNA) that unites Physics-Informed Neural Networks (PINNs) with Robotics Process Automation (RPA) and Digital Twin DT technologies to create an entirely automated, robust robotic motion control system. In contrast to traditional robotic motion control systems that rely on predefined mathematical models or offline training methods, SOPDNA automatically discovers previously unknown physical relationships between robotic motion components and continuously updates the controller policy during operation. The Digital Twin software acts as a nearly identical simulation environment to enable extensive self-experimentation and safety verification of SOPDNA while the RPA software automates all aspects of the SOPDNA learning process, including acquiring experimental data, detecting anomalies, retraining the learning model, and deploying optimised controllers, without requiring any human oversight. The core of SOPDNA consists of a PINN that incorporates a comprehensive mathematical representation of the robot’s physical constraints and uses symbolic regression techniques to estimate unmodelled dynamic variables, such as variable friction coefficients and environmental disturbances. Experimental validation using a robotic manipulator performing trajectory tracking and obstacle avoidance under changing payloads shows the proposed approach’s capabilities. The SOPDNA framework provides an overall trajectory-tracking accuracy of 98.4%, a 22.7% reduction in energy usage, and a 30.9% improvement in disturbance rejection when compared to traditional deep reinforcement learning (DRL) and Proportional-Integral-Derivative (PID)-based control systems. Results from the fault-injection experiments show SOPDNA’s ability to recover from degradation of actuators 41.3% faster than conventional methods. Overall, these findings support the potential for self-organising physics-discovered neural network architectures to enhance the development of robust, adaptive, intelligent robotic motion control systems capable of autonomously executing complex tasks.