We propose DAvinCi, a software framework that provides the scalability and parallelism advantages of cloud computing for service robots in large environments. We have implemented such a system around the Hadoop cluster with ROS (Robotic Operating system) as the messaging framework for our robotic ecosystem. We explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. We implemented the FastSLAM algorithm in Map/Reduce and show how significant performance gains in execution times to build a map of a large area can be achieved with even a very small eight-node Hadoop cluster. The global map can later be shared with other robots introduced in the environment via a Software as a Service (SaaS) Model. This reduces the burden of exploration and map building for the new robot and minimizes it's need for additional sensors. Our primary goal is to develop a cloud computing environment which provides a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem.

In this paper, aiming for mobile robot localization in indoor environments, a loosely coupled architecture, which combines indoor Real-time Locating System (RTLS) and dead reckoning (DR) system, is proposed. Within this architecture, the DR is simple and cost effective but subjected to accumulative error; the indoor RTLS can provide absolute, consistent localization estimates, therefore is complementary to DR sensors. The most outstanding feature of this architecture is that it can easily integrate any type of existing RTLS and DR sensors. Another advantage lies in the fact that this architecture adopts a linear model, thus is exempted from the risk of linearization of non-linear models. In order to verify the proposed architecture, an example localization system consisting of an ultra-wideband (UWB) RTLS system and wheel encoders based DR, on a real robot, is implemented. Experiments have been conducted on this example system and the results have demonstrated its performance.

We present a new concept (named DA vinCi) of distributed agents, sensor networks and an intelligent server catered to the home environment. Instead of a single multi-tasking human-like robot, we propose a team of networked task-specific robotic agents that interface with each other and the environment through a spatial map built by the server. We also highlight how our server will be a proxy for all the human-robot interactions (HRI) in the system and discuss the challenges involved. The paper's title captures the jist of our system where even a tea table can be inexpensively mobilized and interacted with via the DA vinCi architecture.