Mobile Robot
Mobile robots are autonomous systems designed to navigate and interact with their environment, with research focusing on improving their perception, navigation, and manipulation capabilities. Current efforts concentrate on enhancing robustness through sensor fusion (e.g., combining radar and vision data), efficient motion planning guided by natural language instructions or reinforcement learning, and reliable localization using techniques like visual odometry and polygon-based mapping. These advancements are crucial for expanding the applications of mobile robots in diverse fields, including manufacturing, logistics, healthcare, and exploration, by enabling safer, more efficient, and adaptable operation in complex and dynamic settings.
Papers
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
Vishnu Dutt Sharma, Jingxi Chen, Pratap Tokekar
Enhancing Door-Status Detection for Autonomous Mobile Robots during Environment-Specific Operational Use
Michele Antonazzi, Matteo Luperto, Nicola Basilico, N. Alberto Borghese
A Hierarchical Approach to Active Pose Estimation
Jascha Hellwig, Mark Baierl, Joao Carvalho, Julen Urain, Jan Peters