Abstract
The integration of artificial intelligence into the field of robotics enables robots to perform their tasks more meaningfully. In particular, deep-learning methods contribute significantly to robots becoming intelligent cybernetic systems. The effective use of deep-learning mobile cyber-physical systems has enabled mobile robots to become more intelligent. This effective use of deep learning can also help mobile robots determine a safe path. The drivable pathfinding problem involves a mobile robot finding the path to a target in a challenging environment with obstacles. In this paper, a semantic-segmentation-based drivable path detection method is presented for use in the indoor navigation of mobile robots. The proposed method uses a perspective transformation strategy based on transforming high-accuracy segmented images into real-world space. This transformation enables the motion space to be divided into grids, based on the image perceived in a real-world space. A grid-based RRT* navigation strategy was developed that uses images divided into grids to enable the mobile robot to avoid obstacles and meet the optimal path requirements. Smoothing was performed to improve the path planning of the grid-based RRT* and avoid unnecessary turning angles of the mobile robot. Thus, the mobile robot could reach the target in an optimum manner in the drivable area determined by segmentation. Deeplabv3+ and ResNet50 backbone architecture with superior segmentation ability are proposed for accurate determination of drivable path. Gaussian filter was used to reduce the noise caused by segmentation. In addition, multi-otsu thresholding was used to improve the masked images in multiple classes. The segmentation model and backbone architecture were compared in terms of their performance using different methods. DeepLabv3+ and ResNet50 backbone architectures outperformed the other compared methods by 0.21%-4.18% on many metrics. In addition, a mobile robot design is presented to test the proposed drivable path determination method. This design validates the proposed method by using different scenarios in an indoor environment.