Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

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

Robotika kolaboratiboa nabigazio autonomoarekin bihurketa prozesuak egiteko entengabeko lanetan

Capítulo 6.2 confidencial . -- Tesis completa 190 p. -- Tesis censurada 165 p.Proiektu honek bi prototipo ezberdin jasotzen ditu. Alde batetik, nabigazio autonomoa erabiltzen duen AMR prototipo baten garapena erakutsiko da. Bestetik, Mercedes ¿ Benz barnean landuriko tresneria baten ikerketa aurkeztuko da. AMR-ak plataforma mugikor ahaltsuak dira eta hauek barneko nabigazio autonomoa erabiltzen dute, edozein gune ezagunetik mugiarazteko. Horregatik, Gasteizko Ingeniaritza Eskolak halako plataforma baten diseinua burutzen hasi da, lokalizazio algoritmoak lantzeko. Robot mugikor honek elementu industrialak erabiliko ditu eta hauek inteligentzia garapenean zenbait oztopo ezarriko ditu. AMR - ri robot bat atxiki ahal zaio, horregatik Mercedes ¿ Benz barnean elementu komertzialekin AMR bateri robot kolaboratibo bat ezarri zaio. Garapen honek lan postuen efizientziak lantzeko baliagarria izango da eta horretarako robotak etengabeko lanetan mugiarazi, kokatu eta kalitatezko lana burutu behar du