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.

Google's recent acquisition of Boston Dynamics marks its eighth robotics purchase in the past six months, showing Google's "moonshot" robotics vision is more than just a pet project. Boston Dynamics is the most high-profile acquisition, however, instantly adding world-leading robotics capability, including robots that can walk all on their own, to Google's arsenal – as well as significant links to the US military – conjuring images of Skynet and the artificial intelligence-led robot uprising straight out of the 1984 film The Terminator. Boston Dynamics is an engineering and robotics design company that works across a wide range of computer intelligence and simulation systems, as well as large, advanced robotic platforms. The company was created as a technology spin-off from Massachusetts Institute of Technology by Prof Marc Raibert in 1992, then the founder and lead researcher of the Leg Lab – a research group focussed on systems that move dynamically, including legged robots. Raibert describes the Boston Dynamics team as "simply engineers that build robots", but in reality Boston Dynamics is much more than that.

When Steve Mahan was a kid in the 1960s, his mother would sometimes wake him in the early hours of the morning to watch the hours of television coverage preceding the launch of the Mercury space missions. "We would hear about all of the preparations, all of the technology, everything that led up to these moments," Mahan says. "And then we would count down'till you finally got to zero and ignition, and one of those rockets begins bellowing fire and smoke, and slowly begins to creep away from the grapples. Now 63 and having lost his sight, Mahan has become one of those capsule-bound explorers. In October 2015, he became the first member of the public to ride in Google's self-driving pod-like prototype, alone and on public roads.

Ford CEO Mark Fields holds Velodyne Puck LIDAR sensor at a press conference at CES in Las Vegas in January. Carmakers and tech firms competing to develop automated vehicles seek a combination of sensors and cameras that provide maximum perception and visibility of surroundings at a cost that's manageable for mass production. Velodyne, a leading maker of laser-based LiDAR, or Light, Detection and Ranging, sensors, says it has designed a new solid-state version of its technology that provides 3D imaging for automated vehicle systems that will cost less than $50 per unit when manufactured at high volume. That's a fraction of the $8,000 cost of its current mechanical spinning LIDAR devices used in prototype robotic cars. The new design "creates a true solid-state LiDAR sensor, while significantly raising the bar as to what can be expected from LiDAR sensors as far as cost, size and reliability," company founder and CEO David Hall said in a statement.

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.

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.