TY - CHAP A1 - Tilly, Julian A1 - Neeb, Christopher A1 - Bhairapu, Chandu A1 - Mohamed, Fatima A1 - Kachana, Indrasena Reddy A1 - Saravanan, Mahesh A1 - Nguyen Pham, Phuoc T1 - Navigating the Future: an approach of autonomous indoor vehicles N2 - 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. KW - Autonomous vehicles KW - SLAM KW - Indoor localization KW - Reinforcement learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-43401 ER -