In this paper, we present the design and implementation of Rapyuta, an open-source cloud robotics platform. Rapyuta helps robots to offload heavy computation by providing secured customizable computing environments in the cloud. The computing environments also allow the robots to easily access the RoboEarth knowledge repository. Furthermore, these computing environments are tightly interconnected, paving the way for deployment of robotic teams. We also describe three typical use cases, some benchmarking and performance results, and two proof-of-concept demonstrations. Note to Practitioners - Rapyuta allows to outsource some or all of a robot's onboard computational processes to a commercial data center. Its main difference to other, similar frameworks like the Google App Engine is that it is specifically tailored towards multiprocess high-bandwidth robotics applications/middlewares and provides a well-documented open-source implementation that can be modified to cover a large variety of robotic scenarios. Rapyuta supports the outsourcing of almost all of the current 3000+ ROS packages out of the box and is easily extensible to other robotic middleware. A pre-installed Amazon Machine Image (AMI) is provided that allows to launch Rapyuta in any of Amazon's data center within minutes. Once launched, robots can authenticate themselves to Rapyuta, create one or more secured computational environments in the cloud and launch the desired nodes/processes. The computing environments can also be arbitrarily connected to build parallel computing architectures on the fly. The WebSocket-based communication protocol, which provides synchronous and asynchronous communication mechanisms, allows not only ROS based robots, but also browsers and mobiles phones to connect to the ecosystem. Rapyuta's computing environments are private, secure, and optimized for data throughput. However, its performance is in large part determined by the latency and quality of the network connection and the performance of the data center. Optimizing performance under these constraints is typically highly application-specific. The paper illustrates an example of performance optimization in a collaborative real-time 3-D mapping application. Other target applications include collaborative 3-D mapping, task/grasp planning, object recognition, localization, and teleoperation, among others.

This paper presents an architecture, protocol, and parallel algorithms for collaborative 3D mapping in the cloud with low-cost robots. The robots run a dense visual odometry algorithm on a smartphone-class processor. Key-frames from the visual odometry are sent to the cloud for parallel optimization and merging with maps produced by other robots. After optimization the cloud pushes the updated poses of the local key-frames back to the robots. All processes are managed by Rapyuta, a cloud robotics framework that runs in a commercial data center. This paper includes qualitative visualization of collaboratively built maps, as well as quantitative evaluation of localization accuracy, bandwidth usage, processing speeds, and map storage.

The computational requirements of the increasingly sophisticated algorithms used in today's robotics software applications have outpaced the onboard processors of the average robot. Furthermore, the development and configuration of these applications are difficult tasks that require expertise in diverse domains, including software engineering, control engineering, and computer vision. As a solution to these problems, this paper extends and integrates our previous works, which are based on two promising techniques: Cloud Robotics and Software Product Lines. Cloud Robotics provides a powerful and scalable environment to offload the computationally expensive algorithms resulting in low-cost processors and light-weight robots. Software Product Lines allow the end user to deploy and configure complex robotics applications without dealing with low-level problems such as configuring algorithms and designing architectures. This paper discusses the proposed method in depth, and demonstrates its advantages with a case study.

In this paper we present the design and implementation of Rapyuta1, the RoboEarth Cloud Engine. Rapyuta is an open source Platform-as-a-Service (PaaS) framework designed specifically for robotics applications. Rapyuta helps robots to offload heavy computation by providing secured customizable computing environments in the cloud. The computing environments also allow robots to easily access the RoboEarth knowledge repository. Furthermore, these computing environments are tightly interconnected, paving the way for deployment of robotic teams. We also describe specific use case configurations and present some performance results.

This paper introduces the Cubli, a 15×15×15 cm cube that can jump up and balance on a corner. Momentum wheels mounted on three faces of the cube (Fig. 1) rotate at high angular velocities and then brake suddenly, causing the Cubli to jump up. Once the Cubli has almost reached the corner stand-up position, controlled motor torques are applied to make it balance on its corner. This paper tracks the development of the Cubli's one dimensional prototype at ETH Zurich and presents preliminary results.