Adaptive Robotics: Integrating Robotic Simulation, AI, Image Analysis, and Cloud-Based Digital Twin Simulation for Dynamic Task Completion

This research project introduces an innovative approach to adaptive navigation in autonomous robotics by integrating robotics simulation, advanced image analysis, and cloud-based storage of digital twin simulations. The primary objective is to enable robots to dynamically assess their surroundings using AI and pre-simulated data to make informed decisions in unfamiliar scenarios. An autonomous mobile robot platform capable of simulation-based navigation using NVIDIA’s Isaac Simulation software was developed. Real-time environmental awareness was achieved through advanced image processing algorithms, and IoT connectivity was integrated for accessing stored digital twin simulations. AI decision-making algorithms were employed to analyze environmental data and simulation inputs, enabling the robot to dynamically redirect its course or accomplish specific tasks. Results demonstrate the potential for robots to autonomously assess and navigate unfamiliar environments, enhancing their adaptability and efficiency. The study’s significance lies in its contributions to advancing adaptive robotics, improving cost-efficiency, enhancing safety, and conducting simulation-based training to reduce physical testing. By leveraging AI, cloud simulations, and image analysis, this research introduces an innovative approach to enhancing a robot’s adaptability and efficiency in various scenarios, contributing to the ongoing advancement of autonomous robotics.