The goal of utilizing RE should be to identify flaws in your version of the product design and figuring out ways to eliminate the errors and bring out a flawless product eventually.
From iRobot ($199 US): Create 2 is a mobile robot platform built from remanufactured Roomba robots and designed for use by educators, developers and high-school and college-age students. Program or build your own projects or start with our sample projects provided online. Create 2 is ready to go, right out of the box, so there is no need to assemble the drive system or worry about low-level code. Other Create 2 features include: Serial cable sends commands from a computer or other microcontroller to the robot Preprogrammed behaviors can be controlled via Open Interface Commands Built-in sensors allow the robot to react to its environment Drill template on faceplate shows safe drilling areas. Removing the faceplate exposes the serial port. Robot returns to Home Base to dock and recharge. Rechargeable battery charges in three hours. Compatible with Roomba 600 Series accessories including batteries, Home Base®, remote control and Virtual Wall® What are some of the things I can do with iRobot Create 2? Program movements, sounds and the LED display, as well as read all of the robot's onboard sensors Add an external computer or microcontroller with additional sensors and actuators to transform Create into exactly the robot you want. Add a camera to build your own camera bot! Use our 3D printable file to create a storage bin and ensure your additional electronics are safely housed within the robot's chassis... ( details )
From Ishikawa Watanabe Laboratory: We have been developing robotic systems that individually achieve fundamental actions of baseball, such as throwing, tracking of the ball, batting, running, and catching. We achieved these tasks by controlling high-speed robots based on real-time visual feedback from high-speed cameras. Before integrating these abilities into one robot, we here summarize the technical elements of each task... ( site )
For the Harvest Automation engineering team to get an accurate view of what the robot is seeing, we've constructed a tool that we call Mindprobe which collects all the sensor data, interprets it and displays this in a graphical form.
From Wired: Ten years ago, WIRED contributing editor Joshua Davis wrote a story about four high school students in Phoenix, Arizona—three of them undocumented immigrants from Mexico—beating MIT in an underwater robot competition. That story, La Vida Robot, has a new chapter: Spare Parts, starring George Lopez and Carlos PenaVega, opens in January, and Davis is publishing abook by the same title updating the kids’ story. To mark that occasion, WIRED is republishing his original story... ( full article )
From Biomimetics MIT Cheetah project: The high speed legged locomotion of the MIT Cheetah requires high accelerations and loadings of the robot’s legs. Because of the highly dynamic environmental interactions that come with running, variable impedance of the legs is desirable; however, existing actuation strategies cannot deliver. Typically, electric motors achieve their required torque output and package size through high gear ratios. High ratios limit options for control strategies. For example, closed loop control is limited to relatively slow speed dynamics. Series elastic actuation adds additional actuators and increases system complexity and inertia. We believed a better option existed. In the end, we developed a novel actuator, optimal in many applications... ( project homepage ) ( full published article )
DARPA's M3 program is creating and demonstrating novel design tools, fabrication methods and control algorithms to make robots more mobile and better able to manipulate objects in their environment.
From Adafruit : Welcome to the Black Friday sale – 15% off plus all the free items & shipping as you shop! Use code: BLACKFRIDAY on check out. We thought about doing flash sales or complicated codes but that’s a lot of frustrating hoop jumping for everyone, so we came up with what we think is an amazing deal that is straight forward, no stress and valuable – a 15% off discount anything in stock and lots of great free things automatically depending on how much you order. We are currently offering a FREE Adafruit Perma-Proto Half-sized Breadboard PCB for orders over $100, a FREE Trinket 5V for orders over $150, FREE UPS ground (Continental USA) for orders $200 or more, a FREE Pro Trinket 5 V for orders over $250 From Sparksfun: On Cyber Monday (12/1), everything in our Actobotics category is 20% off. Next on 12/1/2014, we are offering hourly flash sales from 7 a.m. to 7 p.m. Mountain Standard Time, with 30-50% off on some of our most popular products. These items have been hand-selected by our employees and are some of our favorite designs! See below for the complete list, so you can plan ahead to snag these great deals. Flash Sales are ONLY valid during their time window. If an item is sitting in your cart and the flash sale for it ends, the price will go back up! There is no combining flash sale orders throughout the day. Flash sales are a “while supplies last” sort of deal (which means no backorders!) - so get ‘em while the getting is good.... ( list of flash sale items ) From Servocity: ( Full pdf flyer for monday's sale ) Two random orders from monday will recieve a "golden ticket" worth $500 of actobotics parts. From Robotshop: ( Full list of sale items available friday through monday ) Free hexbug nano with every purchase.
By using the right suction and the right lips for the job, you really can "kiss it better" before lifting, rather than after.
From the OpenCV Foundation: OpenCV Foundation with support from DARPA and Intel Corporation are launching a community-wide challenge to update and extend the OpenCV library with state-of-art algorithms. An award pool of $50,000 is provided to reward submitters of the best performing algorithms in the following 11 CV application areas: (1) image segmentation, (2) image registration, (3) human pose estimation, (4) SLAM, (5) multi-view stereo matching, (6) object recognition, (7) face recognition, (8) gesture recognition, (9) action recognition, (10) text recognition, (11) tracking. Conditions: The OpenCV Vision Challenge Committee will judge up to five best entries. You may submit a new algorithm developed by yourself or your implementation of an existing algorithm even if you are not the author of the algorithm. You may enter any number of categories. If your entry wins the contest you will be awarded $1K. To win an additional $7.5 to $9K, you must contribute the source code as an OpenCV pull request under a BSD license. You acknowledge that your contributed code may be included, with your copyright, in OpenCV. You may explicitly enter code for any work you have submitted to CVPR 2015 or its workshops. We will not unveil it until after CVPR. Timeline: Submission Period: Now – May 8th 2015 Winners Announcement: June 8th 2015 at CVPR 2015 (full details)
Manual assembly procedures, which previously have been neither ergonomic nor appropriate for automation, can currently be automated in a cost-effective way.
From Extremetech: The K5, built by the Californian company Knightscope, is billed rather euphemistically as an “autonomous data machine” that provides a “commanding but friendly physical presence.” Basically, it’s a security guard on wheels. Inside that rather large casing (it’s 5 foot tall!) there are four high-def cameras facing in each direction, another camera that can do car license plate recognition, four microphones, gentle alarms, blaring sirens, weather sensors, and WiFi connectivity so that each robot can contact HQ if there’s some kind of security breach/situation. For navigating the environment, there’s GPS and “laser scanning” (LIDAR I guess). And of course, at the heart of each K5 is a computer running artificial intelligence software that integrates all of that data and tries to make intelligent inferences... ( full article ) ( knightscope )
Because of the Nov. 14th submission deadline for this years IEEE Conference on Computer Vision and Pattern Recognition (CVPR) several big image-recognition papers are coming out this week: From Andrej Karpathy and Li Fei-Fei of Stanford: We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations... ( website with examples ) ( full paper ) From Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan at Google: Show and Tell: A Neural Image Caption Generator ( announcement post ) ( full paper ) From Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel at University of Toronto: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models ( full paper ) From Junhua Mao, Wei Xu, Yi Yang, Jiang Wang and Alan L. Yuille at Baidu Research/UCLA: Explain Images with Multimodal Recurrent Neural Networks ( full paper ) From Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell at UT Austin, UMass Lowell and UC Berkeley: Long-term Recurrent Convolutional Networks for Visual Recognition and Description ( full paper ) All these came from this Hacker News discussion .
DJI's new Inspire 1 with 4K camera( $2899 USD from DJI ): Aircraft specs: Hovering Accuracy (GPS Mode) Vertical: 1.6' / 0.5 m Horizontal: 8.2' / 2.5 m Maximum Angular Velocity Pitch: 300°/s Yaw: 150°/s Maximum Tilt Angle 35°/s Maximum Ascent/Descent Speed Ascent: 16.4 fps / 5 m/s Descent: 13.1 fps / 4 m/s Maximum Speed 72.2 fps / 22 m/s (Attitude mode; no wind) Maximum Flight Altitude 14,764' / 4,500 m Maximum Wind Speed Resistance 32.8 fps / 10 m/s Maximum Flight Time Up to 18 minutes Camera: Model Name: X3 Designation: FC350 Sensor Sony EXMOR 1/2.3" CMOS Resolution 12.0 MP Lens Field of View: 94° Focal Length (35 mm Equivalent): 20 mm Aperture: f/2.8 Design: 9 elements in 9 groups; aspherical lens element Filters: Anti-distortion filter; UV filter Video Recording UHD (4K): 4096 x 2160: 24p, 25p 3840 x 2160: 24p, 25p, 30p FHD (1080p): 1920 x 1080: 24p, 25p, 30p, 48p, 50p, 60p HD (720p): 1280 x 720: 24p, 25p, 30p, 48p, 50p, 60p Maximum Biterate: 60 Mbp/s File Format Photo: JPEG, DNG Video: MP4 in a .MOV wrapper （MPEG-4 AVC/H.264） Recording Media Type: microSD/SDHC/SDXC up to 64 GB Speed: Class 10 or faster Format: FAT32/exFAT Photography Modes Single shot Burst: 3, 5, 7 frames per second (AEB: 3/5 frames per second; 0.7 EV bias) Time-lapse Operating Temperature 32 to 104°F / 0 to 40°C Gimbal: Model Zenmuse X3 Number of Axes 3-axis Control Accuracy ±0.03° Maximum Controlled Rotation Speed Pitch: 120°/s Pan: 180°/s Controlled Rotation Range Pitch: -90° to +30° Pan: ±330° Angular Vibration Range ±0.03° Output Power Static: 9 W In Motion: 11 W Operational Current Static: 750 mA In Motion: 900 mA Mounting Detachable Video with required "Johnny Ives-alike" introductory speech:
Records 526 to 540 of 1118
Industrial Robotics - Featured Product
Universal Robots is a result of many years of intensive research in robotics. The product portfolio includes the UR5 and UR10 models that handle payloads of up to 11.3 lbs. and 22.6 lbs. respectively. The six-axis robot arms weigh as little as 40 lbs. with reach capabilities of up to 51 inches. Repeatability of +/- .004" allows quick precision handling of even microscopically small parts. After initial risk assessment, the collaborative Universal Robots can operate alongside human operators without cumbersome and expensive safety guarding. This makes it simple and easy to move the light-weight robot around the production, addressing the needs of agile manufacturing even within small- and medium sized companies regarding automation as costly and complex. If the robots come into contact with an employee, the built-in force control limits the forces at contact, adhering to the current safety requirements on force and torque limitations. Intuitively programmed by non-technical users, the robot arms go from box to operation in less than an hour, and typically pay for themselves within 195 days. Since the first UR robot entered the market in 2009, the company has seen substantial growth with the robotic arms now being sold in more than 50 countries worldwide.