@inproceedings{TillyNeebBhairapuetal., author = {Tilly, Julian and Neeb, Christopher and Bhairapu, Chandu and Mohamed, Fatima and Kachana, Indrasena Reddy and Saravanan, Mahesh and Nguyen Pham, Phuoc}, title = {Navigating the Future: an approach of autonomous indoor vehicles}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-43401}, pages = {12}, abstract = {In this project, we explored the ability of Reinforcement learning (RL) in driving an indoor car autonomously. RL has proven its good performance in solving challenging decision-making problems. Therefore, RL can be a promising solution for autonomous car to deal with complex driving scenarios. As hardware a model car eqipped with sensors and powerful computational unit has been used. We also utilized SLAM for environment mapping and a combination of lidar data and Wi-Fi technology for localization. The experiment showed that the model can perform very well in simulation. Although the model lacks the ability to drive the car as smoothly along a route, the car is still able to avoid obstacles and walls in an unknown real-world environment.}, language = {en} }