The Intel RealSense cameras have been gaining in popularity for the past few years for use as a 3D camera and for visual odometry. I had the chance to hear a presentation from Daniel Piro about using the Intel RealSense cameras generally and for SLAM (Simultaneous Localization and Mapping). The following post is based on his talk.
I had somebody ask me questions this week about underwater photography and videography with robots (well, now it is a few weeks ago…). I am not an expert at underwater robotics, however as a SCUBA diver I have some experience that can be applicable towards robotics.
I had the opportunity to attend the National Robot Safety Conference for Industrial Robots today in Pittsburgh, PA (USA). Today was the first day of a three-day conference. While I mostly cover technical content on this site; I felt that this was an important conference to attend since safety and safety standards are becoming more and more important in robot system design. This conference focused specifically on industrial robots. That means the standards discussed were not directly related to self-driving cars, personal robotics, or space robots (you still don’t want to crash into a martian and start an inter-galactic war).
We are only in the earliest stages of so-called algorithmic regulation – intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws – but it already has profound implications for the relationship between private citizens and the state.
The need for fast, accurate 3D mapping solutions has quickly become a reality for many industries wanting to adopt new technologies in AI and automation. New applications requiring these 3D mapping platforms include surveillance, mining, automated measurement & inspection, construction management & decommissioning, and photo-realistic rendering. Here at Clearpath Robotics, we decided to team up with Mandala Robotics to show how easily you can implement 3D mapping on a Clearpath robot.
Update: The response to Tertill’s crowdfunding campaign has amazed and delighted us! Pledges totalling over $250,000 have come from 1000+ backers. We’re shipping to all countries, with over a fifth of Tertill’s supporters coming from outside the United States. But the end is near; Tuesday (11 July) is the last full day of the campaign. After that Tertill’s discounted campaign price will no longer be available and delivery in time for next year’s (northern hemisphere) growing season cannot be assured.
Franklin Robotics has launched a Kickstarter campaign for Tertill, their solar-powered, garden-weeding robot.
We are excited to show off a simulation of a Prius in Mcity using ROS Kinetic and Gazebo 8. ROS enabled the simulation to be developed faster by using existing software and libraries. The vehicle’s throttle, brake, steering, and transmission are controlled by publishing to a ROS topic. All sensor data is published using ROS, and can be visualized with RViz.
The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free.
The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson. Below are lessons from inverse kinematics and robot motion.
When developing algorithms for coordinating the behaviors of swarms of robots it is crucial that the algorithms are actually deployed and tested on real hardware platforms. Unfortunately, building and maintaining a swarm robotics testbed is a resource-intense proposition and, as a consequence, resources rather than ideas tend to be the bottleneck and swarm robotics research does not progress at the rate it could. The Robotarium sets out to remedy this problem by providing remote access to a large team of robots, where users can upload their code, run the experiments remotely, and get the scientific data back. This article describes the structure and architecture of the Robotarium as well as discusses what constitutes an effective, remotely accessible research platform.