Cloud Robotics
Cloud robotics leverages cloud computing resources to overcome the limitations of onboard robot processing power, enabling complex tasks and large-scale deployments. Current research focuses on efficient task allocation and scheduling algorithms, often utilizing machine learning models for improved performance and adaptability in diverse applications like warehousing and retail. This approach significantly enhances robotic capabilities, particularly in areas requiring high computational demands or large robot fleets, leading to improved efficiency and scalability in various industries.
Papers
FogROS2-PLR: Probabilistic Latency-Reliability For Cloud Robotics
Kaiyuan Chen, Nan Tian, Christian Juette, Tianshuang Qiu, Liu Ren, John Kubiatowicz, Ken Goldberg
Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers
Achilleas Santi Seisa, Sumeet Gajanan Satpute, George Nikolakopoulos