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
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
Dario Mantegazza, Alessandro Giusti, Luca Maria Gambardella, Jérôme Guzzi
Guiding vector fields for the distributed motion coordination of mobile robots
Weijia Yao, Hector Garcia de Marina, Zhiyong Sun, Ming Cao
Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception Attacks
Pritam Dash, Guanpeng Li, Mehdi Karimibiuki, Karthik Pattabiraman
Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information
Matthew Cleaveland, Esen Yel, Yiannis Kantaros, Insup Lee, Nicola Bezzo