Towards cloud robotic system: A case study of online co-localization for fair resource competence

The cloud transforms the potential of robotics, which enable poor-equipped robots to fulfill complex tasks. Robots are relieved from hardware limitation, while large amount of available resources and parallel computing capability are available in the “cloud”. We implemented a data management system using Twisted-based server-client platform and Robotic Operating System (ROS), aiming at co-localization of cloud robots. However, resource competition is pervasive for practical applications of networked robotics. As a major bridge, the limited bandwidth becomes a bottleneck needs to be considered for the architecture design. We propose an infrastructure which considers multi-robot autonomous negotiation (MRAN) module. The framework is validated by enabling several poor-equipped robots to retrieve location data from a dynamically updated map which is built by a well-equipped robot. Experiment results demonstrate that the proposed framework is feasible for current robotic applications. Furthermore, it achieves better performance under resource competition, and optimizes Quality of Service (QoS) using a shared network with limited bandwidth.