Researchers from Yokohama National University created a highly precise mobile robot with a wide range of motion using piezoelectric actuators, achieving path errors of under 0.5-4.75 µm. The robot's performance demonstrated its suitability for precise positioning and wide transportation of objects of various sizes.
Apple iPhone 17 Pro
Apple iPhone 17 Pro delivers top performance and advanced cameras for field documentation, data collection, and secure research communications.
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.
Researchers developed an intelligent FLC designer to create stable solar sail attitude control without a priori knowledge or excessive manual workload. The IFLCD uses neural network modelling and automatic design method to solve the black-box and time-varying control problem, making it suitable for unmanned and complicated systems.
Apple MacBook Pro 14-inch (M4 Pro)
Apple MacBook Pro 14-inch (M4 Pro) powers local ML workloads, large datasets, and multi-display analysis for field and lab teams.
Nagoya University scientists developed a controller with a sleep mode to procure energy only when needed, reducing the need for storage batteries and capacitors. This innovative solution enables efficient energy saving and promotes the practical application of power packet type energy Internet.
A team of researchers has developed a MEMS scanning lidar that can detect objects reliably even in shaky environments. The long-range MEMS lidar prototype uses a digital controller to suppress errors caused by vibrations, allowing for stable 3D imaging and object detection.
Jeff Shamma, a King Abdullah University of Science & Technology professor, has been elected as an IFAC Fellow for his significant contributions to linear parameter varying systems and multiagent systems. He joins a prestigious list of global academics and experts in the field of control and systems engineering.