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
The Multi-Trip Autonomous Mobile Robot Scheduling Problem with Time Windows in a Stochastic Environment at Smart Hospitals
Lulu Cheng, Ning Zhao, Kan Wu, Zhibin Chen
HoloBots: Augmenting Holographic Telepresence with Mobile Robots for Tangible Remote Collaboration in Mixed Reality
Keiichi Ihara, Mehrad Faridan, Ayumi Ichikawa, Ikkaku Kawaguchi, Ryo Suzuki
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Peter Amorese, Morteza Lahijanian
Investigating the Usability of Collaborative Robot control through Hands-Free Operation using Eye gaze and Augmented Reality
Joosun Lee, Taeyhang Lim, Wansoo Kim
Accuracy evaluation of a Low-Cost Differential Global Positioning System for mobile robotics
Christian Blesing, Jan Finke, Sebastian Hoose, Anneliese Schweigert, Jonas Stenzel