Auto World Modelling in Kautham for Motion Planning Problems

Abstract

Auto World Modelling in Kautham for Motion Planning Problems provides path finding solution in a dynamic environment using 3D models and Real-Time object detection with the help of Artificial Intelligence. The 3D models are generated for surface reconstruction, segmentation and model fitting. This mechanism is integrated with Real-Time object detection models which applies neural network to the acquired data. The data is then collected from hardware sensors including stereo and time of flight cameras. The process is supplemented by key points extracted from the object detections. Thus, providing better visualization and Automatic Recognition for path finding and model fitting. Models and Spatial data are then fed to the Linux based motion planning tool known as Kautham integrated with Linux framework, Robotics Operating System (ROS). ROS is used for Motion Planning, Navigation and Interaction with Robots. The Algorithms generated Kautham are then fed to ROS, enabling Robots to navigate using the most optimal path. The Union of Object detection Models (Yolo | Res-Net50) and 3D auto generated world scenes with ImageAI (python library) enables further room for precision and geometric complexity to be used in optimized planning for Kautham. This Project has limitless potential in Automating World Model construction with motion planning for wide variety of Robots currently working in fields and future deployments for Real-Time Scenarios.