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
Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
Adam Rashid, Chung Min Kim, Justin Kerr, Letian Fu, Kush Hari, Ayah Ahmad, Kaiyuan Chen, Huang Huang, Marcus Gualtieri, Michael Wang, Christian Juette, Nan Tian, Liu Ren, Ken Goldberg
Autonomous Monitoring of Pharmaceutical R&D Laboratories with 6 Axis Arm Equipped Quadruped Robot and Generative AI: A Preliminary Study
Shunichi Hato, Nozomi Ogawa
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields
Nicolaj Schmid, Cornelius von Einem, Cesar Cadena, Roland Siegwart, Lorenz Hruby, Florian Tschopp
Development of control algorithms for mobile robotics focused on their potential use for FPGA-based robots
Andrés-David Suárez-Gómez, Andres A. Hernandez Ortega
Pushing in the Dark: A Reactive Pushing Strategy for Mobile Robots Using Tactile Feedback
Idil Ozdamar, Doganay Sirintuna, Robin Arbaud, Arash Ajoudani
Collision-Free Platooning of Mobile Robots through a Set-Theoretic Predictive Control Approach
Suryaprakash Rajkumar, Cristian Tiriolo, Walter Lucia
Rollover Prevention for Mobile Robots with Control Barrier Functions: Differentiator-Based Adaptation and Projection-to-State Safety
Ersin Das, Aaron D. Ames, Joel W. Burdick