Articles tagged with Robot Navigation
Researchers demonstrated a breakthrough in microrobotics: swarms of magnetic microrobots can manipulate objects without physical contact by harnessing fluid-generated torque. The microrobots act as motors to move millimeter-sized passive objects, opening new pathways for precision manufacturing and biomedical applications.
A hierarchical 3D LiDAR localization method improves robot positioning in large outdoor spaces even after seasonal changes. The method integrates deep learning techniques to extract discriminative local features from 3D point clouds, making it robust to environmental variability.
Researchers at the University of Pennsylvania have developed HoloRadar, a system that enables robots to reconstruct hidden 3D spaces beyond their line of sight using radio waves processed by AI. This capability can improve safety and performance in driverless cars and cluttered indoor settings.
Couzin's research focuses on quantitative behavioural biology, using advanced technology to study collective motion and decision-making. He has challenged conventional views of collective motion and influenced fields like robotics and the social sciences with his widely cited publications.
Scientists created biologically realistic artificial cilia using hydrogel, enabling precise control over their motion. The tiny structures can be powered by low-voltage electrical signals and have shown remarkable durability and versatility.
Researchers developed soft robots inspired by manta rays, utilizing magnetic fields to move, recharge power supply, and perform tasks autonomously. The magnets stabilize electrochemical reactions in flexible batteries, enhancing performance and efficiency.
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.
Frasky, developed by IIT, combines robotics and AI to address environmental sustainability and labor shortages in agriculture. The robot can navigate autonomously, map its surroundings, and apply selective treatments to grape clusters with precision.
A research team developed an optimization method for path planning in uncertain environments using deep reinforcement learning and action curiosity module. The algorithm showed remarkable improvements in convergence speed, training duration, and path planning success rate compared to baseline algorithms.
Researchers found that Earth's gravity pulls down on sand more strongly than previously thought, making it more rigid and reducing the likelihood of getting stuck. This discovery highlights the importance of physics-based simulation in analyzing rover mobility on granular soil.
New humanoid surgical robots offer a solution to the growing healthcare labor shortage, allowing for more efficient surgeries and reduced burnout among doctors. By taking on routine tasks, these robots can free up surgeons to focus on more complex procedures.
Researchers at IIT have successfully demonstrated the first flight of a humanoid robot, iRonCub3, which can lift off the floor and maintain stability. The robot's AI-powered control system enables it to handle high-speed turbulent airflows, extreme temperatures, and complex dynamics.
Researchers developed a new robot navigation system called LENS, which uses brain-inspired computing to set a low-energy benchmark for robotic place recognition. The system combines a spiking neural network with a special camera and low-power chip to enable fast and energy-efficient location tracking.
Researchers at KAIST developed a quadrupedal navigation system that enables the robot to reach its target destination quickly and safely in complex terrain. Inspired by cat's paw placement, they significantly reduced computational complexity.
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.
The University of Oldenburg has secured funding for three research clusters: Hearing4all, Ocean Floor, and NaviSense. These clusters aim to improve hearing loss prediction, diagnosis, and treatment, as well as animal navigation research. The funding enables the continuation of high-quality research with social relevance.
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.
Researchers create CARL-Bot to ride vortex rings and navigate turbulent ocean currents without fighting them, inspired by nature's ability to conserve energy. The system uses a single accelerometer and simple control laws to achieve energy-efficient propulsion, opening doors for future applications in ocean exploration and monitoring.
A soft robot can carry loads through the air along established tracks, navigating angles of up to 80 degrees and carrying loads up to 12 times its weight. The robot uses infrared light to move along the track, repeating a rolling motion that pulls it forward.
Computer scientists at the University of Bath reprogrammed a Roomba to perform four new tasks, showcasing the untapped potential of domestic robots. The researchers identified over 100 ways to tap into the latent potential of robotic devices, proposing functions such as playing with pets, watering plants, and delivering breakfast in bed.
A new robotic framework allows robots to learn tasks by watching a single how-to video, significantly reducing the time and energy needed for training. The RHyME system enables robots to adapt to real-world environments and perform multiple-step sequences with improved success rates.
Researchers at Osaka Metropolitan University developed an autonomous driving algorithm for robots to navigate raised cultivation beds, utilizing lidar point cloud data. The system enables precise movement and accuracy in both virtual and actual environments, promising to expand tasks beyond harvesting to monitoring and pruning.
Researchers from Osaka Metropolitan University developed an explainable AI model for ship navigation that explains the basis for its decisions and intentions using numerical values. This technology aims to increase trust among maritime workers and contribute to the realization of unmanned ships.
Researchers at the University of Amsterdam found that worms behave like 'active polymers' when navigating complex environments. In disordered obstacles, they spread faster as obstacle density increases, contradicting common sense. The study's findings suggest a crucial role for environmental geometry in dictating movement strategies.
Researchers developed a tiny magnetic robot that can take 3D scans from deep within the body, enabling 'virtual biopsies' for non-invasive cancer diagnosis and treatment. The device uses high-frequency imaging to create detailed 3D reconstructions, allowing clinicians to diagnose and treat colorectal cancer in a single procedure.
A cutting-edge solution integrates Visible Light Positioning and Inertial Navigation Systems to tackle dynamic inclination changes and signal blockages. The tightly coupled system achieves impressive positioning accuracy of up to 10 cm and inclination precision within 1 degree.
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 developed a novel bio-hybrid drone by integrating robotic technology with biological odor sensors from insects, overcoming visual sensor limitations. The drone's enhanced performance enables accurate odor detection and tracking, broadening applications in gas sensing, disaster response, and rescue operations.
Researchers developed a compact swimming robot that can maneuver through tight spaces and transport heavier payloads. The robot uses silently undulating fins to propel itself and achieves impressive speeds of 12 centimeters per second.
Researchers at UCF aim to create mobile robotic assistants that offer easier control and a better robot-human interface. The project, supported by a $600,000 grant, will focus on testing and user feedback, with initial trials conducted with students and adults with upper body paralysis.
MIT researchers have introduced a new system called MiFly that enables drones to self-localize in indoor, dark, and low-visibility environments. The system uses radio frequency waves reflected by a single tag placed in the environment, allowing the drone to estimate its trajectory with high accuracy.
Researchers are developing a software framework for crowd-sourced 3D map generation and visual localization from camera data to improve real-time updates and low-cost visual localization. This technology aims to advance self-driving vehicles and enable fully automated transportation
Researchers studied rats navigating an L-shape track with a food-guarding robot. The rats created neurological maps of places to avoid after experiencing negative events and thought about these locations even after leaving the area. This finding provides insight into the neuroscience of common psychological conditions like anxiety.
Cricket frogs use a combination of underwater push and leg movement to propel themselves out of the water, resulting in a 'belly flop' motion. This discovery could lead to advancements in bio-inspired robotics and water testing systems.
Researchers from Singapore, Japan and US developed an advanced swarm navigation algorithm for cyborg insects that prevents them from getting stuck in challenging terrain. The new algorithm represents a significant advance in swarm robotics and could pave the way for applications in disaster relief and search-and-rescue missions.
Researchers developed a soft robot with fins shaped like manta rays, capable of swimming up and down throughout the water column. The robot uses spontaneous snapping-induced jet flows to achieve high speeds and maneuverability.
Researchers developed a platform to help AI learn complex tasks through nuance and real-time instruction, achieving up to a 30% increase in success rates. The GUIDE framework allows humans to provide ongoing, nuanced feedback, fostering incremental improvements and deeper understanding.
A study at Hospital for Special Surgery found that both robotic-assisted navigation and augmented reality techniques demonstrate superior accuracy in pedicle screw placement, with 99.6% of robotic screws rated as Grade A or B and 92.6% of augmented reality screws achieving the top rating.
A QUT research team developed a novel place recognition algorithm using Spiking Neural Networks (SNNs) to improve robotic navigation efficiency and accuracy. The system achieved an improvement of 41% in place recognition accuracy, enabling robots to adapt to appearance changes over time.
Path planning is vital for navigating micro-/nanorobots in complex and dynamic environments. Micro-/nanorobots play a significant role in advancing medical and biological applications, enabling targeted drug delivery and minimally invasive surgery.
NeuroMechFly v2 simulates how a fruit fly navigates through its environment while reacting to sights, smells, and obstacles. The model can track moving objects visually or navigate towards an odor source, while avoiding obstacles in its path, enabling researchers to study brain-body coordination and animal intelligence.
Scientists at Max Planck Institute for Intelligent Systems developed a novel method for deploying multiple magnetic miniature robots to navigate through complex networks resembling blood vessels. The system allows for simultaneous treatment of multiple locations, saving critical time and enabling localized care.
Scientists at Princeton University develop a system of two robots connected by flexible tether, enabling them to solve complex problems like maze navigation and object gathering. The innovative approach harnesses physical characteristics rather than digital calculation to achieve remarkable abilities.
Researchers developed grain-sized soft robots that can transport up to four different drugs, release them in reprogrammable orders and doses, and navigate complex environments inside the human body. The robots' precision functions have the potential to significantly improve therapeutic outcomes while minimizing side effects.
Researchers at GIST developed a cat's eye-inspired vision system that filters out unnecessary light and improves visibility in low-light conditions. The system promises to elevate the precision of drones, security robots, and self-driving vehicles, enabling them to navigate intricate environments with unparalleled accuracy.
Researchers have developed an octopus-inspired adhesive that can quickly grab and controllably release challenging underwater objects. The adhesive achieves high attachment strength on various surfaces, including rough and curved ones, within a fraction of a second.
Researchers developed a light-driven, toroidal micro-robot that can navigate complex environments like medicine and environmental monitoring. The innovation uses liquid crystalline elastomer to overcome viscous forces and enables autonomous movement in low Reynolds number regimes.
Automated systems with machine learning capabilities improve diagnostic accuracy and workflow efficiency in laboratory medicine. However, ensuring data security, preventing biases in ML models, and maintaining transparency are key concerns.
Researchers at the University of South Australia have developed an AI sensor that can accurately measure the orientation of the Milky Way in low light, using a technique inspired by the dung beetle. This system could improve navigation for drones and satellites in difficult lighting conditions.
Researchers have developed a complex algorithm to choreograph the movement of two robotic arms, ensuring a clear space between them and maintaining a consistent magnetic field. This innovative system uses two robotic arms to steer magnetic medical devices, such as vine robots, with precise control and accuracy.
Researchers at TU Delft developed an insect-inspired navigation strategy for tiny, lightweight robots, allowing them to return home after long trajectories while requiring minimal computation and memory. The strategy combines visual breadcrumbs and step counting to enable autonomous navigation in cluttered environments.
UCF researchers George Atia and Yue Wang received a $1.2 million DARPA grant to develop AI-based technologies that can help autonomous systems adapt to unknown variables and overcome simulation-to-real gap issues.
The University of Maryland team created a camera mechanism that mimics the involuntary movements of the human eye, resulting in sharper and more accurate images. The Artificial Microsaccade-Enhanced Event Camera (AMI-EV) has implications for robotics, national defense, and industries relying on accurate image capture.
A new surgical procedure reconnects muscles in the residual limb, allowing patients to receive proprioceptive feedback about their prosthetic limb's position. Seven patients who underwent this surgery were able to walk faster, avoid obstacles, and climb stairs more naturally than those with traditional amputations.
Researchers from MIT and the MIT-IBM Watson AI Lab devised a navigation method that converts visual representations into pieces of language, which are then fed into one large language model. The approach utilizes purely language-based representations, generating synthetic training data to overcome challenges in visual data availability.
Researchers developed a technique called Multi-View Attentive Contextualization (MvACon) to improve AI's ability to map 3D spaces using 2D images from multiple cameras. MvACon significantly improved the performance of vision transformers in locating objects and detecting speed and orientation.
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
Researchers developed a novel methodology utilizing digital twins to establish the usefulness of built environment design guidelines for robots. Digital twins allow for real-time monitoring, hazard identification, and training a robot's algorithm before deployment.