AI-AUGMENTED VIRTUAL-REAL INTEGRATED SIMULATION CONTROL is a B.Tech project topic for Mechatronics Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
AI-AUGMENTED VIRTUAL-REAL INTEGRATED SIMULATION CONTROL Project Details
| Abstract |
This project provides implementation support for developing an integrated virtual-real simulation and control framework designed for industrial robotic manipulators. Addressing the critical challenges of complex trajectory optimization, kinematic singularities, and the sim-to-real gap, the proposed framework utilizes a multi-layer pipeline spanning modeling, artificial intelligence optimization, virtual verification, and physical execution. The architecture incorporates a hybrid Genetic Algorithm and Reinforcement Learning (GA-RL) trajectory planner alongside a fast neural-network-based inverse kinematics solver to bypass singularity bottlenecks. Additionally, a Deep Deterministic Policy Gradient (DDPG) adaptive controller is integrated to suppress position disturbances during real-time operations. The system is designed to run on a high-frequency Hardware-in-the-Loop (HIL) simulation platform, utilizing real-time communication protocols such
as EtherCAT to synchronize virtual models with physical 6-DOF robotic manipulators. Guidance is provided for modeling the manipulator dynamics, training the neural network solvers, configuring the real-time control loops, and generating embedded code to achieve high-precision tracking under external disturbances. This structured approach assists students in understanding the deployment of advanced AI-driven control algorithms on physical hardware.
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| Reference Paper |
AI-AUGMENTED VIRTUAL-REAL INTEGRATED SIMULATION AND CONTROL SYSTEM FOR INDUSTRIAL ROBOTIC MANIPULATORS |
| Domain |
Mechatronics Engineering |
| Sub-Domain |
Embedded Control Systems |
| PDF Download |
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