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
Deep Reinforcement Learning for Mobile Robot Path Planning
Hao Liu, Yi Shen, Shuangjiang Yu, Zijun Gao, Tong Wu
Learning-based Methods for Adaptive Informative Path Planning
Marija Popovic, Joshua Ott, Julius Rückin, Mykel J. Kochenderfer
Sound Matters: Auditory Detectability of Mobile Robots
Subham Agrawal, Marlene Wessels, Jorge de Heuvel, Johannes Kraus, Maren Bennewitz