DAvinCi: A cloud computing framework for service robots

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.

Authors

• Rajesh Arumugam
• Vikas Reddy Enti
• Bingbing Liu
• Xiaojun Wu
• Krishnamoorthy Baskaran
• Foo Kong Foong
• Appadorai Senthil Kumar
• Dee Meng Kang
• Wai Kit Goh

Keywords

• Cloud computing
• Service robots
• Robot sensing systems
• Clustering algorithms
• Ecosystems
• Scalability
• Parallel processing
• Operating systems
• Parallel robots
• Performance gain
• Parallelization
• Global Map
• Computer Cluster
• Software Framework
• Robot Operating System
• Robots In Environments
• Feature Maps
• Computational Resources
• Sensor Data
• Path Planning
• Collective Learning
• Posterior Mode
• File System
• Acceptable Time
• Storage Resources
• Algorithm Execution
• Cloud Environment
• Map Tasks
• Hadoop Distributed File System
• Back-end System
• Master Node
• Particle Path
• Private Cloud
• Onboard Computer
• External Entities
• Robotic Network
• Proof Of Concept
• Single Node