Concentric Tube Robots (CTRs) have garnered significant interest within the surgical robotics community because of their flexibility, dexterity, and ease of miniaturization. However, mastering the unique kinematics and design principles of CTRs can be challenging for newcomers to the field. In this paper, we present an educational kit aimed at lowering the barriers to entry into concentric tube robot research. Our goal is to provide accessible learning resources for CTRs, bridging the knowledge gap between traditional robotic arms and these specialized devices. The proposed kit includes (1) An open-source design and assembly instructions for an economical (cost of materials $\approx$ 700 USD) modular CTR; (2) A set of self-study materials to learn the basics of CTR modeling and control, including automatically-graded assignments. To evaluate the effectiveness of our educational kit, we conducted a human subjects study involving first-year graduate students in engineering. Over a four-week period, participants -- none of whom had any prior knowledge of concentric tube robots -- successfully built their first CTR using the provided materials, implemented the robot's kinematics in MATLAB, and conducted a tip-tracking experiment with an optical tracking device. Our findings suggest that the proposed kit facilitates learning and hands-on experience with CTRs, and furthermore, it has the potential to help early-stage graduate students get rapidly started with CTR research. By disseminating these resources, we hope to broaden participation in concentric tube robot research to a wider a more diverse group of researchers.

In this work, we present a novel target-based lidar-camera extrinsic calibration methodology that can be used for non-overlapping field of view (FOV) sensors. Contrary to previous work, our methodology overcomes the non-overlapping FOV challenge using a motion capture system (MCS) instead of traditional simultaneous localization and mapping approaches. Due to the high relative precision of the MCS, our methodology can achieve both the high accuracy and repeatable calibrations of traditional target-based methods, regardless of the amount of overlap in the field of view of the sensors. We show using simulation that we can accurately recover extrinsic calibrations for a range of perturbations to the true calibration that would be expected in real circumstances. We also validate that high accuracy calibrations can be achieved on experimental data. Furthermore, We implement the described approach in an extensible way that allows any camera model, target shape, or feature extraction methodology to be used within our framework. We validate this implementation on two target shapes: an easy to construct cylinder target and a diamond target with a checkerboard. The cylinder target shape results show that our methodology can be used for degenerate target shapes where target poses cannot be fully constrained from a single observation, and distinct repeatable features need not be detected on the target.

This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.

This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.

Connected cars and the Internet of Things go together like peanut butter and jelly. But realizing the future of autonomous vehicles will demand close attention to be paid to cybersecurity, functional-safety standards, and other critical factors. IoT will advance the era of self-driving cars, which currently is dominated by Tesla Motors. At the same time, it will change some of the dynamics in this market. On one hand, it will turn automotive manufacturers into technology companies, which could provide new revenue streams for carmakers. On the other hand, it will open the door for new players that have never had a viable entry point in the automotive market. Consider the case of Velodyne LiDAR, a Morgan Hill, Calif.-based company, which last month opened a factory in nearby San Jose to manufacture its LIDAR product.

Qawiyah Muhammad can see her own future. An Uber driver in Pittsburgh, she knows that one day her job will be replaced by a robot car. She knows the robot cars are coming because she sometimes spots experimental models driving themselves around town. "You can tell them apart," she said, "because they have a thing on the top of the car, like'Back to the Future.' " There's a reason they stand out so much, and it's not because Uber or anybody else thinks they look cool. On top of Uber's new driverless cars is an array of bulky sensors – cameras, radars, lidars – that eventually will be shrunk into a more discreet system that will replace Muhammad and thousands of other Uber drivers.

A company founded in 1984 to produce high-end audio systems is now on the vanguard of the self-driving vehicle frontier with key financial backing from Ford and Baidu, China's largest search engine provider. Velodyne, a Morgan Hill, Calif.-based company, just received 150 million from Ford and Baidu to continue development and production of Lidar, or the 3D light-powered radar that helps self-driving cars see where they are going. With a presence on three continents, Velodyne is now regularly mentioned in research reports that cite leading companies in the niche field of lidar. Lidar emits short pulses of laser light so that software in the self-driving vehicle can create a real-time, high-definition 3D image of what's around it. In addition to cars, the systems also have growing potential for agricultural equipment, mining vehicles and military vehicles.

Ford on Tuesday announced plans to release fully autonomous and driverless ride-sharing vehicles by 2021. Ford CEO Mark Fields, speaking at the automaker's Research and Innovation center in Palo Alto, also pledged to double the company's Silicon Valley workforce and expand its facilities by 150,000 square feet by the end of this year. Additionally, Ford and Chinese search engine company Baidu are investing a combined 150 million in Velodyne LiDAR, makers of light, detection and ranging technology for 3D digital imaging. For self-driving cars, Velodyne's technology uses a combination of light, cameras and laser-based sensors to assess a surrounding environment and create a 360-degree view that can be used for mapping, localization, object identification and collision avoidance. The Morgan Hill, Calif.-based company wants to use the funding to improve design, expand production and accelerate mass adoption of its technology.

Ford and Chinese search engine company Baidu will each invest 75 million in Velodyne, a company that makes laser sensors that help guide self-driving cars. Velodyne, based in Morgan Hill, California, says it will use the 150 million investment to expand design and production and reduce the cost of its sensors. Laser sensors -- called Lidar, which stands for light, detection and ranging -- can also be used in conventional vehicles as part of driver assist systems such as automatic emergency braking.

If you want a clear idea of the boom in testing of autonomous vehicles, look to Velodyne Acoustics Inc., a Morgan Hill, Calif., maker of the all-important laser range finder that gives driverless cars their sight. Velodyne, the world's biggest supplier of the laser sensor used on autonomous vehicles in testing, expects to double its sensor sales in 2016 and has just begun shipping a new version that will cost as little as 500 at...