106 research outputs found
FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2
Mobility, power, and price points often dictate that robots do not have
sufficient computing power on board to run contemporary robot algorithms at
desired rates. Cloud computing providers such as AWS, GCP, and Azure offer
immense computing power on demand, but tapping into that power from a robot is
non-trivial. We present FogROS2, an open-source platform to facilitate cloud
and fog robotics that is compatible with the emerging Robot Operating System 2
(ROS 2) standard. FogROS2 is completely redesigned and distinct from its
predecessor FogROS1 in 9 ways, and has lower latency, overhead, and startup
times; improved usability, and additional automation, such as region and
computer type selection. Additionally, FogROS2 was added to the official
distribution of ROS 2, gaining performance, timing, and additional improvements
associated with ROS 2. In examples, FogROS2 reduces SLAM latency by 50 %,
reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning
28x. When compared to FogROS1, FogROS2 reduces network utilization by up to
3.8x, improves startup time by 63 %, and network round-trip latency by 97 % for
images using video compression. The source code, examples, and documentation
for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and
is available through the official ROS 2 repository at
https://index.ros.org/p/fogros2
FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity
The Robot Operating System (ROS2) is the most widely used software platform
for building robotics applications. FogROS2 extends ROS2 to allow robots to
access cloud computing on demand. However, ROS2 and FogROS2 assume that all
robots are locally connected and that each robot has full access and control of
the other robots. With applications like distributed multi-robot systems,
remote robot control, and mobile robots, robotics increasingly involves the
global Internet and complex trust management. Existing approaches for
connecting disjoint ROS2 networks lack key features such as security,
compatibility, efficiency, and ease of use. We introduce FogROS2-SGC, an
extension of FogROS2 that can effectively connect robot systems across
different physical locations, networks, and Data Distribution Services (DDS).
With globally unique and location-independent identifiers, FogROS2-SGC securely
and efficiently routes data between robotics components around the globe.
FogROS2-SGC is agnostic to the ROS2 distribution and configuration, is
compatible with non-ROS2 software, and seamlessly extends existing ROS2
applications without any code modification. Experiments suggest FogROS2-SGC is
19x faster than rosbridge (a ROS2 package with comparable features, but lacking
security). We also apply FogROS2-SGC to 4 robots and compute nodes that are
3600km apart. Videos and code are available on the project website
https://sites.google.com/view/fogros2-sgc.Comment: 9 pages, 8 figure
DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet
Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.info:eu-repo/semantics/publishedVersio
Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning
Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers
Advanced Automation for Space Missions
The feasibility of using machine intelligence, including automation and robotics, in future space missions was studied
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