Convolutional neural network based path navigation of a differential drive robot in an indoor environment

Abstract

The current work illustrates a vision-guided approach to a real-time robot navigation system and the implementation of Faster Convolutional Neural Networks (FCNN) to train and detect objects with multiple datasets of the mobile robot and obstacles. The algorithm keeps monitoring the distance between the obstacles and generates way points in-between the obstacles in such a way that a path is created towards the target. Thus, the shortest path for navigation is created which checks for possible errors and update the path during execution, making it an AI system. This approach reduces the need for incorporating multiple EMU sensors on the mobile robot and transfers the computation process to a remote processor. The processor and mobile robot communicate wirelessly for simultaneous localization and path planning. While the algorithm is being executed, trained objects are detected from each frame captured by the camera which is used to develop path by avoiding the obstacles. The performance of the system is evaluated by conducting multiple experiments with different mapping regions.