Articles tagged with Control Systems
MIT researchers developed a new system-on-a-chip called Gleanmer, which generates highly accurate 3D maps of the robot's environment using Gaussians to represent obstacles. This approach reduces memory and power consumption by up to 99%, making it suitable for lightweight augmented reality headsets.
Scientists created a shape-changing flying robot named Floaty that can fly efficiently while staying stable in the air. By adjusting its flaps to control air resistance, Floaty balances itself and recovers from disturbances.
Researchers propose embedding AI foundation models into control software to enable robot swarms to achieve new levels of autonomy and adaptability. This enables robots to switch between tasks in real-time and interact more naturally with humans.
Researchers at Duke University introduce Argus, a 20-eyed robot with no front or back, demonstrating dynamic symmetry and improving performance across various measures. The design surpasses the theoretical maximum of 0.6, enabling robustness, energy efficiency, and resilience to damage.
Experts warn that AI-enhanced surgical robotics could enable true personalized surgery and enhance surgical team performance. However, regulatory reforms are needed to address risks from adaptive systems and ensure patient benefits.
The study, led by Lucy Liu and L. Mahadevan, shows that adding the right amount of noise to individual robot movements can ease gridlock and improve efficiency in crowded environments. The researchers used computer simulations and experiments with small robots to test their ideas.
Researchers developed magnetically controlled microrobots made from diatoms to target glioblastoma lesions with photodynamic therapy. The microrobots achieved a significant cytotoxic effect on primary glioblastoma cells and demonstrated good biocompatibility.
A cross-sectional study found that smartphone use during school hours is associated with reduced cognitive control in youths aged 11-18. The study highlights the need for school-level policies and digital literacy programs to address habitual smartphone-checking behaviors that fragment attention.
Researchers developed a new AI control system that allows soft robots to learn a broad set of motions once and adapt instantly to changing conditions without retraining. The system combines structural learning with real-time adaptiveness, making it suitable for diverse tasks and environments.
Researchers at Harvard University have developed a new design method for optimizing rolling contact joints in robots, which can lead to better grippers, assistive devices, and more efficient robotic movement. The optimized joints performed spectacularly, correcting misalignment by 99% in knee-assist devices.
Researchers at Stanford University have successfully used machine-learning-based control to operate a robot on the International Space Station, allowing for faster and more efficient navigation while maintaining safety. The system, which includes a warm start feature, enables robots like Astrobee to move through complex environments wi...
Scientists at TU Delft developed an algorithm allowing multiple autonomous drones to work together to control and transport heavy payloads even in harsh weather. The system enables drones to lift and orient a payload with precision, ideal for reaching infrastructure like offshore wind turbines.
A team of engineers at Harvard John A. Paulson School of Engineering and Applied Sciences designed a proof-of-concept walking robot using only four moving parts connected by rubber bands and powered by one motor. The robot can find its way through mazes, avoid obstacles, and sort objects by mass without electronic control systems.
Researchers propose a novel control scheme to address challenges in traditional backstepping control designs and event-triggered control. The scheme significantly reduces communication bandwidth usage, offering a new approach for efficient control of complex industrial systems.
The book explores foundational and advanced principles of modeling concurrent control systems using Petri nets, focusing on building reliable, verifiable systems where concurrency plays a central role.
Researchers at NTU Singapore have developed an AI-powered robotic system to assemble cyborg insects for search and rescue efforts, significantly reducing preparation time and human error. The automated system can attach electronic backpacks to Madagascar hissing cockroaches in under 8 minutes, about 30 times quicker than manual methods.
Researchers create aerially transforming morphobot ATMO to address complex aerodynamic forces near ground level. The system uses advanced control method model predictive control to adapt quickly to changing dynamics during transformation.
Aerial robots are limited to manipulating rigid objects, but Lehigh University researcher David Saldaña aims to expand their capabilities with an adaptive controller and reinforcement learning. His research has potential applications in construction, disaster response, and industrial automation.
Researchers at Duke University developed a novel framework called WildFusion that fuses vision, vibration and touch to enable robots to sense complex outdoor environments like humans do. The system was tested in real-world settings and showed remarkable ability to accurately predict traversability and improve robot decision-making.
Researchers developed novel haptic devices to enable precise robot control with tactile feedback, reducing collisions and improving user proficiency. The devices integrate digital twin technology and augmented reality for enhanced immersion.
The Purdue team developed Purdubik’s Cube, a high-speed robotic system that solves a Rubik’s Cube in record-breaking 0.103 seconds. The team leveraged machine vision, custom solving algorithms, and industrial-grade motion control hardware to achieve this feat.
Researchers at MIT created a table tennis bot that can return shots with high-speed precision, achieving a hit rate of 88% in tests. The technology could be adapted to improve the speed and responsiveness of humanoid robots for search-and-rescue operations.
Researchers developed an intelligent autonomous robot capable of automating hospital disinfection processes through dual disinfection system, increasing efficiency and precision. The robot's performance was validated through real-world testing, reducing the risk of infection in hospitals.
The observation pyramid uses different robot platforms to survey ocean areas, enabling flexible task management. Autonomous robotic organizations will reduce costs, improve mission efficiency and increase system robustness.
The Harvard RoboBee has been equipped with crane fly-inspired legs and an updated controller, allowing it to land safely on various surfaces. The robot's delicate actuators were protected by the improved design, which enabled controlled landing tests on a leaf and rigid surfaces.
Researchers developed electronics-free robots that can walk without electronics, using compressed gas as a power source. The robots were printed in one go from standard 3D printing material and demonstrated three-day operation with air pressure control.
Research by University of Toronto's Rotman School of Management found that adoption of international auditing standards leads to better financial reporting. Countries with stronger enforcement and more full integration of ISA into domestic practices show the most positive impact on audit quality.
The Harvard robot uses latch-mediated spring actuation to jump high and cover long distances relative to its size. It combines walking and jumping modes for effective navigation in natural environments.
Researchers at UC3M developed a new soft joint model that enables versatility of movement, adaptability, and safety in robots. The asymmetrical triangular structure allows for greater bending angles with less force, providing operational protection and increased safety in human-robot interactions.
Researchers develop innovative hybrid control strategy to improve product yields in biosynthetic processes. The new approach combines model-based optimization with in-cell feedback control, outperforming traditional methods and promising reduced costs and environmental impact.
Researchers from Tohoku University and partners developed a decentralized control system to analyze plesiosaur locomotion, accounting for motion adjustment. The system successfully recreated coordinated flippers patterns in response to changes in flapping cycle and morphology.
Researchers at the University of Pennsylvania have developed a new consensus complementarity control (C3) algorithm that allows robots to react to complex physical contact in real-time. The algorithm enables robots to control the motion of sliding objects, a challenging task previously thought to be impossible for autonomous robots.
The Human AugmentatioN via Dexterity (HAND) center aims to develop robots capable of enhancing human labor through engineered systems of dexterous robotic hands, AI-powered fine motor skills, and human interface. The center's goal is to make robotic assistance accessible and applicable to a wide range of physical actions.
Researchers developed a learning-based approach called LVWS to schedule robots and enable voluntary waiting, resulting in faster task completion. This method outperformed other approaches by up to 23% suboptimality, demonstrating its potential to improve manufacturing, agriculture, and warehouse automation.
Researchers at Delft University of Technology use drone racing to test neural-network-based AI control systems planned for next-generation space missions. The goal is to achieve optimal onboard operations by continuously replanning trajectories, reducing resource consumption and boosting mission autonomy.
A new type of dart launcher has been developed as a safer and more cost-effective alternative to firearms or air guns for injecting animals with drugs. The prototype uses electromagnetic coils and lidar technology to deliver a projectile with controlled kinetic energy.
A recent study published in Science Robotics found that robots struggle to outperform biological organisms in foot races. The researchers analyzed data from dozens of studies and concluded that the failure of robots to outrun animals is not due to shortfalls in individual components, but rather inefficiencies in system design.
Researchers at Georgia Tech have developed a universal approach to controlling robotic exoskeletons that requires no training, calibration, or adjustments. The system uses deep learning to autonomously adjust assistance levels for walking, standing, and climbing stairs, reducing user effort and metabolic expenditure.
A team of researchers developed a time-optimal control method for autonomous ships, optimizing maneuvering performance in dynamic sea conditions. The new control system accounts for various forces affecting ship movement and can improve efficiency and safety, reducing shipping costs and carbon emissions.
A team at Zhejiang University has developed a self-driving cloaked unmanned drone with an intelligent aeroamphibious invisibility cloak, capable of manipulating electromagnetic scattering in real-time across dynamic environments. The cloak integrates perception, decision-making, and execution functionalities using spatiotemporal modula...
A new control system optimizes predictive models with real-time observations, predicting fusion plasma behavior with high accuracy. This approach enables adaptive predictive control in uncertain conditions, laying the foundation for fusion reactor control.
The new Repoint technology features a lift and move mechanism with passive locks, ensuring fault-tolerant operation even when two actuators fail. It has been successfully tested at a test track, demonstrating compatibility and functionality with conventional switch rail arrangements.
A soft, wearable robot was used to help a person living with Parkinson’s disease walk without freezing, eliminating the debilitating symptom and allowing them to regain their independence. The device provided instantaneous effects and consistently improved walking in a range of conditions.
A new technology enables the printing of complex robots with soft, elastic, and rigid materials in one go. This allows for the creation of delicate structures and parts with cavities as desired.
Yu Yang's NSF-funded research aims to reduce vehicle emissions and promote the use of electric bikes and scooters by developing socially informed traffic signal control systems. The project involves a three-pronged method that uses low-cost mobile air-quality sensing, spatial-temporal graph diffusion learning, and reinforcement learnin...
The project aims to assess the operational resilience of microgrids on DoD installations and ships, using new operational resilience indexes developed by Lehigh University researcher Javad Khazaei. The team will develop a dashboard to monitor resilience indexes in real-time, providing recommendations for improving the systems.
Researchers from the University of Oldenburg developed a new stochastic method to mitigate sudden swings in wind turbine power output. The study found that control systems are mainly responsible for short-term fluctuations and can be optimized to ensure more consistent energy output.
Researchers developed a groundbreaking soft valve technology that integrates sensors and control valves into soft robots, eliminating the need for electric components. This innovation enables safe operation underwater or in environments with sparks risks, reducing weight burdens and costs.
Researchers developed a new method for controlling lower limb exoskeletons using deep reinforcement learning, enabling more robust and natural walking control. The system has the potential to benefit users with spinal cord injuries, multiple sclerosis, stroke, and other neurological conditions.
A novel technique has been proposed to enhance the learning ability of robots performing repetitive tasks by using a fractional power update rule. The study demonstrates fast convergence rates and potential applications in industries such as autonomous vehicles and rehabilitation robots.
The TOAST project, led by Aarhus University, aims to develop the Tactile Internet technology and upscale the talent pool for this emerging field. Researchers will focus on edge intelligence, haptic communication, and machine learning to create immersive user experiences.
A team of researchers at Tohoku University has developed an artificial intelligence-driven contact control system to minimize friction between moving parts in machinery, aiming to reduce wear and tear and extend lifespan.
A new algorithm was proposed to predict a satellite's attitude without sensor data, crucial for debris tracking and damage reduction. The method uses quaternions and an extended Kalman filter to estimate the satellite's state, followed by a neural network to extract unmodeled environmental torque.
Researchers at NTNU developed a predictive control heating system that can optimize energy consumption by predicting heating needs and utilizing surplus heat. The system saved 1.8% in energy costs per month, demonstrating its potential to reduce electric bills.
A new control system design method achieves ultra-precision positioning on industrial machinery with unknown vibration characteristics. The system uses a nominal characteristic trajectory following controller and a vibration suppression compensator to reach target positions with high speed and precision.
AQT at Berkeley Lab organized a workshop on classical control systems for quantum computing, bringing together industry leaders and researchers to share experimental control advances. The workshop highlighted the need for advanced features in classical control electronic systems to optimize quantum computer performance.
University of Missouri engineers are working on a collaborative human-robot order picking system to speed up the online delivery process. The proposed model aims to optimize key decisions in warehouse operations, allowing robots to navigate efficiently and collaborate with humans to increase efficiency.
A new study from the University of East Anglia found that employees who perceive managerial controls as restrictive and punitive may develop dysfunctional responses, including workplace deviance and deliberate ignorance. The study suggests that threatening managerial controls have different effects on skilled and non-skilled workers, w...
A new CNN framework, PE-Net, is proposed for predicting machine remaining useful life (RUL) accurately. The framework uses a novel architecture with small-sized one-dimensional convolution kernels and deep networks to learn features from input time series signals.