Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Graphene “Tattoos” for Plants Could Form Neural Networks
Researchers at the University of Texas at Austin have developed an innovative graphene "tattoo" that adheres directly to plant leaves, enabling real-time monitoring of leaf hydration. This breakthrough, published in the journal Nano Letters in February, addresses the limitations of traditional methods that require cutting leaves for moisture assessment. The sensor, which functions like a three-terminal transistor, sends electric pulses into the leaf, allowing it to measure moisture levels without disrupting photosynthesis. Led by associate professor Jean Anne Incorvia and graduate student Utkarsh Misra, the team envisions a future where these sensors could form a neural network across forests, providing critical data on drought and fire risks. The flexible and nearly transparent graphene material allows the tattoo to adapt to the leaf's movements, while its unique properties enable it to act as an artificial synapse, potentially enhancing plant-based computing. The concept emerged from a collaboration with geologist Ashley Matheny, highlighting the practical applications of the technology in agriculture and environmental monitoring. The researchers successfully trained a neural network to classify leaf hydration states, paving the way for more sophisticated plant monitoring systems that could help farmers and forest rangers respond to climate change challenges.
Graphene Agriculture Wildfires Neural-networks
Soft Graphene Muscle Enables Robots to Maintain Stability for Over 13 Hours
Researchers from Sun Yat-sen University and Tsinghua University have developed a soft robot capable of maintaining stability against disturbances for over 13 hours. This innovation utilizes an ultrathin soft muscle, known as Soft Graphene Muscle (SGM), which integrates self-sensing, electrothermal actuation, and disturbance control without the need for external sensors. The significance of this development lies in its potential to enhance the operational capabilities of soft robots in real-world environments. Traditional soft robots often struggle with stability due to their flexible structures, which can amplify disturbances. The SGM's ability to adaptively balance objects heavier than itself marks a significant advancement in soft robotics, moving closer to practical applications. Future developments to watch include the potential for further integration of sensing and control within soft materials, as well as the implications for deploying soft robots in complex environments. The research was published in eScience, highlighting the collaborative efforts of experts in biomedical engineering and integrated circuits from both universities.
Soft Robotics Adaptive Control Robotics Engineering AI Material Science
Graphene-based sensor to improve robot touch
Researchers have made significant advancements in robotic technology by developing a miniature tactile sensor designed to enhance the touch capabilities of robots. Despite improvements in vision and movement, touch has remained a critical limitation for robotic systems. This innovative sensor, which is part of a sensing array on a robotic manipulator, utilizes multiscale-structured materials to provide enhanced tactile feedback. The development aims to bridge the gap in robotic sensory perception, allowing robots to interact more effectively with their environments. This breakthrough was reported recently, highlighting the ongoing efforts to improve robotic functionality and adaptability in various applications.
Robotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
