Browse Topic: Robotics
Biomanufacturing uses microorganisms to produce chemicals or materials of interest, much like a brewery uses fermentation by yeast to produce the alcohol in beer. Biomanufacturing relies upon synthetic biology to reprogram yeast or other microorganisms to produce something of greater value, such as fuel, food, or pharmaceuticals. Industrial biomanufacturing has made significant advances and the products it can deliver include reactive coatings and textiles, sensors, optical materials that can bend light, and new therapeutics such as antimicrobials and vaccines. The convergence of synthetic biology, robotics, and artificial intelligence is opening the way to produce materials never before possible in the commercial market. These same technologies create the opportunity for the miniaturization of this technology to fit into ever more compact spaces, bringing forward deployment of these mini-factories closer and closer to the point of need.
The economic logic behind factory software upgrades is breaking down. For the past four decades, automating a factory has meant one thing: buying into a large industrial software platform. Manufacturers turned to established suppliers such as Rockwell Automation and Siemens for the control systems, execution layers, and quality software that run their plants, millions of lines of rigorously engineered code, developed under strict standards and wrapped in guardrails. That rigor was never optional. When software commands a robot arm, a stamping press, or a paint line, a defect is not a bug ticket; it is a safety incident. The engineering discipline behind these platforms reflects that reality, and has served the industry well. But discipline at that scale comes at a price. Platform deployments are measured in millions of dollars and years of integration work, which means they only make economic sense for the largest, most repeatable problems a plant faces. Everything else, the hundreds of smaller, plant-specific pain points that erode margins every day, has largely gone unaddressed. Running a modern factory became expensive by design.
Researchers have received a $24.9 million grant to develop an NSF Materials Innovation Platform where universities, national laboratories and industry members can run alloy-discovery campaigns using robotic systems and artificial intelligence. Texas A&M University, College Station, Texas A six-year, $24.9 million grant from the U.S. National Science Foundation (NSF) will establish the Autonomous Robotic Metallurgist Materials Innovation Platform (ARM-MIP) at Texas A&M University - a national user facility where robots and artificial intelligence take on the repetitive labor of alloy discovery, freeing scientists to focus on discovery itself. ARM-MIP will be sited at The Texas A&M University System RELLIS Campus in Bryan, a dedicated research campus that also hosts the Texas A&M Engineering Experiment Station and the U.S. Army Transformation and Training Command's central testing hub at the George H.W. Bush Combat Development Complex. The platform is designed to reach more than 200 users a year and 50 alloys per month in its first year, scaling to more than 200 alloys per month by year six.
Extreme winter weather often leads to ice accretion on transmission lines. Manual removal is inefficient, costly, and poses safety risks. To address this issue, this paper presents the design of a de-icing robot to replace manual operations for transmission line de-icing. The main content focuses on the detailed structural design of the robot, including the mobile platform, de-icing mechanism, and adaptive adjustment module. Finite element simulations are conducted on key components to verify the structural rationality and the correctness of material selection. The proposed de-icing robot enhances the safety of the de-icing process, improves operational efficiency, and provides a valuable reference for transmission line de-icing methods, demonstrating significant practical value.
A dispatch from the automaker's “let's just try it” city finds hydrogen fuel cells, automated parking robots, and a robust spirit of innovation. For a planned high-tech community created by an automaker, Woven City is not laser-focused on automobiles and transportation. In fact, to get a glimpse of Toyota's priorities here, look to the pedestrian stoplights at intersections. They remain a constant green, until the connected city senses an approaching vehicle and turns them red. This focus on pedestrians over vehicles is one small part of Toyota's rebranding as a mobility company. Toyota announced Woven City at CES 2020, converting its recently closed Higashi-Fuji factory, which built over 7.5 million Toyota vehicles during a 53-year run, into a blank slate for new technologies, automotive and otherwise.
Physical AI refers to applications in which AI technologies are connected to hardware that sense and execute actions in the physical world, allowing systems to autonomously act and adapt in real time. It spans automotive, robotics, industrial automation, smart infrastructure, aerospace, healthcare devices, software-defined machines, and more. Regardless of application, they have one thing in common: they must operate safely, reliably, and predictably in real world environments. Unlike purely digital AI, these systems are constrained by embedded electronics, timing, power, safety, and system-level interactions that are difficult to validate early.
Spinoff is NASA’s annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
Humanoid robots have long been the focus of science fiction, but today they are making their way into industrial environments thanks to the simultaneous maturing and convergence of multiple systems. Technology advances have driven the development of humanoid robots that have a wide range of movement and can perform demanding jobs around the clock without tiring. While currently representing a small share of all industrial robot deployments, the humanoid robot market is projected to grow rapidly over the next few years. In fact, estimates suggest the market could reach over $4 billion by 2030. This growth is being driven by factors such as labor shortages, falling costs, and the need for more flexible automation.
Humanoid robots are moving beyond hype-driven prototypes toward early commercial deployment, with automotive manufacturing emerging as the first scalable adoption market, according to IDTechX’s recent report Humanoid Robots 2026-2036: Technologies, Markets, and Opportunities.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Traditional industrial robotics has been built on traditional premises: define the task precisely, program the motion, and repeat it with minimal variation. This model has delivered reliability, speed, and scale across multiple application domains.
There's a well-known video from San Francisco in 1906 that comes up repeatedly in mobility discussions here in the 21st Century. If you haven't seen A Trip Down Market Street, it depicts the absolute bonkers variety of transportation methods used on Market Street back then: cable cars, horsecars, streetcars, pedestrians, automobiles and more. Past is prologue in a world that is adding scooters, delivery robots and other last-minute delivery vehicles to our streets. At the 2026 New York International Auto Show in April, Honda displayed its latest option in the form of the Fastport eQuad Prototype. The eQuad was originally unveiled at Eurobike 2025 and technically comes from Fastport, a micromobility venture from the Honda New Business Innovation Lab that was established to work on projects with global logistics companies. Jamie Davies, chief of operations for Fastport, called the group a kind of startup within Honda. “Three years ago,” Davies told SAE Media in New York, “a small group of Honda associates [came] together and [said], Okay, how can we create a new value for the company, a new business vertical? And so we've run the project in an agile way, working with customers all along the way to understand what their needs are, what the requirements are, and to bring to market something that fits.”
ABB Robotics is integrating NVIDIA Omniverse libraries into ABB Robotics’ RobotStudio ® to help manufacturers deploy physical AI in real-world robotics applications.
Friendly robots, the ones people love to love, are quirky: R2-D2, C-3PO, WALL-E, BB-8, Marvin, Roz and Baymax. They’re emotional, prone to panic or bossy, empathetic and able to communicate like humans do — even when they communicate in only beeps and bloops.
Researchers in Carnegie Mellon University’s Robotics Institute (RI) have designed a system that makes an off-the-shelf quadruped robot nimble enough to walk a narrow balance beam.
Picture a futuristic swarm of robots deployed on a time-sensitive task, like cleaning up an oil spill or assembling a machine. At first, adding robots is advantageous, since many hands make light work. But a tipping point comes when too many crowd the space, getting in each other’s way and slowing the whole task down.
Smart implants that not only stabilize a fracture but also monitor the healing process from day one — and deliver targeted support when required — are currently being developed at Saarland University by a team of engineers, medical researchers, and computer scientists. The engineering team led by Paul Motzki is contributing shape-memory micro-actuators with integrated sensing capabilities, while Bergita Ganse and her research group provide the medical expertise in fracture healing.
While many manufacturers are enjoying demand for their products and have innovations in the pipeline, their growth strategy can sometimes stall. The issue might be due to workforce shortages, which can evolve from temporary setbacks to long-term challenges. For a variety of industries, helping hands are often hard to find, and it’s an issue that’s been consistent in recent years. Given this, many companies are considering transitioning to automation, including through the use of collaborative robots or cobots, for short.
Under a microscope, a bouquet of lollipop-like structures, each smaller than a grain of sand, waves gently in a petri dish of liquid. Suddenly, they snap together, like the jaws of a Venus flytrap, as a scientist waves a small magnet over the dish. What was previously an assemblage of tiny passive structures has transformed instantly into an active robotic gripper.
Through a technology partnership that breaks new ground in the machine tool industry, Siemens offers an automation solution for the busy, multi-tasking, small to mid-sized machine shop, as it combines a digital twin of the software and programming of its popular SINUMERIK 828 CNC, working in tandem with a KUKA robot, to simplify the operation and programming in part handling for the machine tool operator.
Snake-like robots represent the future of rescue. Their slender bodies allow them to navigate narrow spaces, uneven terrain, and water surfaces, entering places that would be hazardous for humans. This could potentially save lives in earthquake-prone areas, like Japan.
The future of Moon exploration may be rolling around a non-descript office on the CU Boulder campus. Here, a robot about as wide as a large pizza scoots forward on three wheels. It uses an arm with a claw at one end to pick up a plastic block from the floor, then set it back down.
Medical imaging technology is advancing rapidly, bringing new opportunities and challenges for machine designers. Systems that once required dedicated hospital rooms and significant floor space are becoming more compact, faster, mobile and capable of delivering increasingly detailed clinical insight. From advanced CT scanners to imaging platforms integrated with surgical robotics, imaging equipment is evolving toward more point-of-care (POC) solutions to meet rising expectations for diagnostic accuracy, procedural guidance and operational efficiency.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm that the novel technique achieves better performance both in accuracy and computation efficiency. Also, this mixed control system can deal with complex moving path for track target object. Even when meet different obstacle and not sure measurement, it still works well with other moving obstacle in many conditions. This can be strong to face other dynamic obstacle even if have different situation with changing obstacle and uncertain data. It shows that this paper works as an attempt toward optimal solution to combine the model-based technique together with data-driven approach aiming to support real-time, highly accurate, adaptive prediction is based control technique, promising applications into industry and promoting more improved works related.
The global automotive industry has reached a new era. If 2025 was defined by the cautious exploration of “experimental pilots” and the collection of vast data lakes from connected vehicle fleets, 2026 marks the year that data finally gains a mind of its own within the assembly plant. We are witnessing a transition from passive automation to integrated, agentic autonomy. This is a shift that moves beyond simple programmed robotic arms and toward systems capable of independent reasoning and real-time optimization. This evolution is not just a technical upgrade; it is a fundamental restructuring of how vehicles are built, de-risked, and scaled in an increasingly volatile global economy.
Items per page:
50
1 – 50 of 2193