In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values. ; This research was financed by the plant of Mercedes-Benz Vitoria through the PIF program to develop an intelligent production. Moreover, The Regional Development Agency of the Basque Country (SPRI) is gratefully acknowledged for their economic support through the research project “Motor de Accionamiento para Robot Guiado Automáticamente”, KK-2019/00099, Programa ELKARTEK.
Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot
2020-05-28
doi:10.3390/math8050855
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot
BASE | 2020
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