It's been several months, but NASA has finally prized the lid off the capsule that returned the Bennu asteroid to Earth. NASA engineers removed two metal fasteners from the TAGSAM robotic arm that were keeping the lid stuck and trapping the precious cargo inside. Now the business of analyzing the entirety of the 250g sample for clues about the history of the solar system can begin. It was back in October 2020 that the robotic arm aboard the OSIRIS-REx spacecraft nabbed a handful of the Bennu asteroid. Space fans rejoiced in September last year when the craft finally returned to Earth, marking the end of the 1.16 billion mission, one of NASA's most ambitious ever.

Disassembly of electric vehicle batteries is a critical stage in recovery, recycling and re-use of high-value battery materials, but is complicated by limited standardisation, design complexity, compounded by uncertainty and safety issues from varying end-of-life condition. Telerobotics presents an avenue for semi-autonomous robotic disassembly that addresses these challenges. However, it is suggested that quality and realism of the user's haptic interactions with the environment is important for precise, contact-rich and safety-critical tasks. To investigate this proposition, we demonstrate the disassembly of a Nissan Leaf 2011 module stack as a basis for a comparative study between a traditional asymmetric haptic-'cobot' master-slave framework and identical master and slave cobots based on task completion time and success rate metrics. We demonstrate across a range of disassembly tasks a time reduction of 22%-57% is achieved using identical cobots, yet this improvement arises chiefly from an expanded workspace and 1:1 positional mapping, and suffers a 10-30% reduction in first attempt success rate. For unbolting and grasping, the realism of force feedback was comparatively less important than directional information encoded in the interaction, however, 1:1 force mapping strengthened environmental tactile cues for vacuum pick-and-place and contact cutting tasks.

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.

Smart machines, businesses as platforms, and the gig economy figure into Tom Friedman's riff on where the future of work could take global companies. Tom Friedman is a Pulitzer Prize-winning columnist for The New York Times and author of seven best-selling books. His work covers a broad range of topics, including globalization, the Middle East, and environmental challenges. Friedman is intrigued by the connections among people, businesses, technology, and institutions that shape the evolution of an increasingly complex world. His newest book, "Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations," explores the future of work, a topic also being examined by Deloitte U.S. and Deloitte LLP's Center for the Edge.

Sept. 03--Production of Boeing's large 777 twinjet in Everett is significantly backed up, with incomplete jobs on each aircraft forcing catch-up work, some of which is being finished only after the jets roll out onto the airfield. Scrambling to fix the mess, they've kept 777 deliveries on track only by working long overtime hours, including weekends, with just two days off a month. Workers blame the new 777 robotic fuselage assembly system that management has been ramping up. This critical new technology, which Boeing must get right before the forthcoming 777X, automates the precise drilling and fastening together of fuselage panels for the big moneymaking jet. Boeing executives insist the robotics -- known as Fuselage Automated Upright Build, or FAUB -- are not the major hang-up.

The factory floor is a marvel of automation. With a press of a button, the whole place can seem to run itself. But although today's factories use automated workflows, process change is still mostly manual. When demands arise in an industrial environment, managers and engineers must interrupt the automation to update the processes that make the machines go. Now, thanks to machine learning algorithms, it's becoming possible for smart software to scrutinize data from a variety of sources -- sensors on machines or changes in supply chains, for instance -- and redesign processes in real time.