Articles tagged with "bio-inspired-robotics"
Robotic fins mimic stingrays for stable, precise underwater movement
Researchers at the University of California, Riverside, have developed robotic fins that mimic stingray swimming to better understand how these animals achieve stable and precise underwater movement. Stingrays, which dwell near the seabed, use undulatory, wave-like fin motions, while pelagic rays like manta rays flap their fins in smooth, oscillatory motions suited for open water. By testing robotic fins in a water tunnel simulating ocean flow, the team discovered an unexpected "unsteady ground effect": near the seafloor, rays experience negative lift that pulls them downward, unlike birds or airplanes that gain lift near the ground. However, a slight upward tilt of the fins, as observed in real rays, counteracts this negative lift, enabling stable swimming close to the seabed. The study also found that undulatory swimming provides better ground clearance and stability than purely oscillatory fin motions, helping benthic rays avoid collisions with the ocean floor. These insights suggest that the distinct swimming styles of rays are evolutionary adaptations for maintaining
roboticsunderwater-robotsbio-inspired-roboticsrobotic-finsunderwater-vehiclesmechanical-engineeringaquatic-roboticsShape-shifting coral that stiffens in seconds to transform robotics
Researchers at the University of Pennsylvania have discovered that the Pacific soft coral Leptogorgia chilensis can rapidly shift its skeleton from soft to stiff by employing a natural granular jamming mechanism. This coral’s skeleton is composed of millions of uniquely shaped calcium carbonate particles called sclerites, suspended in a gelatinous matrix. When the coral is disturbed, it expels water, causing the gel to shrink and the sclerites to compact and interlock, instantly stiffening the coral’s branches. This biological example of granular jamming—previously observed only in non-living materials like sand—demonstrates a novel natural adaptation where hard mineral particles jam together to provide protection. The study, led by doctoral student Chenhao Hu and associate professor Ling Li, used advanced imaging, computer modeling, and mechanical testing to analyze how the coral’s sclerites interlock under pressure. The distinctive rod-like shape of the sclerites with branching outgrowths allows them to jam tightly when compressed, enabling the
roboticsmaterials-sciencegranular-jammingshape-shifting-materialsbio-inspired-roboticscalcium-carbonateadaptive-materialsFalcon-inspired robot achieves bird-like takeoff with wing motion
Scientists in China have developed RoboFalcon2.0, a falcon-inspired flying robot that achieves bird-like takeoff through a novel flapping-sweeping-folding (FSF) wing motion. Unlike conventional robotic flyers that use fixed wings or rotors, RoboFalcon2.0 mimics the natural wing movements of birds by flapping, sweeping forward, and folding its wings in a coordinated rhythm. This reconfigurable wing system, enabled by mechanical decouplers and a lightweight frame, allows the robot to generate lift and control pitch effectively during takeoff. Wind tunnel tests and simulations demonstrated that sweeping the wings forward amplifies leading-edge vortices, enhancing lift and stabilizing pitch, which is critical for successful liftoff. Weighing 800 grams with a 1.2-meter wingspan, RoboFalcon2.0 captures the dynamics of small birds and replicates the high power consumption pattern observed in living birds during takeoff. Field tests confirmed smooth self-powered take
robotbio-inspired-roboticsflapping-wing-robotaerial-roboticsrobotic-flightbiomimicryautonomous-takeoffUS scientists achieve robot swarm control inspired by birds and fish
US scientists have developed a novel framework for controlling robotic swarms inspired by the collective behaviors of birds, fish, and bees. The research addresses a central challenge in swarm robotics: creating a decentralized control mechanism that allows robots to coordinate effectively without a central leader. By introducing a new geometric design rule based on a quantity called “curvity,” which acts like an intrinsic charge influencing how robots curve in response to external forces, the team demonstrated that assigning positive or negative curvity values to individual robots can govern their interactions. This curvature-based control enables the swarm to exhibit different collective behaviors such as flocking, flowing, or clustering. The researchers successfully validated their approach through experiments showing that these simple, physics-inspired rules scale from pairs of robots to thousands, and can be embedded directly into the mechanical design of robots. This method simplifies swarm control from a complex programming challenge into a material science problem, potentially broadening applications from large industrial or delivery robots to microscopic robots used in medical treatments like targeted drug delivery.
robotswarm-intelligencedecentralized-controlartificial-intelligenceroboticsswarm-roboticsbio-inspired-roboticsRobots can sense when something might slip from grip with new method
Engineers at the University of Surrey have developed a novel, bio-inspired method enabling robots to sense and prevent objects from slipping during manipulation by predicting slip events and adjusting their movements in real-time. Unlike traditional robotic grip strategies that rely solely on increasing grip force—which can damage delicate items—the new approach mimics human behavior by modulating the robot’s trajectory, such as slowing down or repositioning, to maintain a secure hold without excessive squeezing. This method, demonstrated through a predictive control system powered by a learned tactile forward model, allows robots to anticipate slip risks continuously and adapt accordingly. The research, published in Nature Machine Intelligence, shows that trajectory modulation significantly outperforms conventional grip-force-based slip control in certain scenarios and generalizes well to objects and movement paths not included in training. This advancement holds promise for enhancing robotic dexterity and reliability across various applications, including healthcare (handling surgical tools), manufacturing (assembling delicate parts), logistics (sorting awkward packages), and home assistance. The study highlights the importance of
roboticsrobotic-manipulationslip-preventionautomationtactile-sensingpredictive-controlbio-inspired-roboticsWorld’s first robot dog learns animal gaits in 9 hours with AI power
Researchers at the University of Leeds have developed the world’s first robot dog capable of autonomously adapting its gait to mimic real animal movements across unfamiliar terrains. Using an AI system inspired by animals such as dogs, cats, and horses, the robot—nicknamed “Clarence”—learned to switch between walking styles like trotting, running, and bounding within just nine hours. This bio-inspired deep reinforcement learning framework enables the robot to adjust its stride for energy efficiency, balance, and coordination without human intervention or additional tuning, even in environments it has never encountered before. This breakthrough represents a significant advancement in legged robotics, with practical applications in hazardous environments like nuclear decommissioning and search and rescue, where human presence is risky. By training the robot entirely in simulation and then transferring the learned policies directly to the physical machine, the researchers achieved a high level of adaptability and resilience. The project also underscores the potential of biomimicry in robotics, offering insights into how biological intelligence principles can improve robotic
robotAIroboticslegged-robotsbio-inspired-roboticsautonomous-robotsrobot-dogBees’ secret to learning may transform how robots recognize patterns
Researchers at the University of Sheffield have discovered that bees actively shape their visual perception through flight movements, rather than passively seeing their environment. By creating a computational model mimicking a bee’s brain, they showed that bees’ unique flight patterns generate distinct neural signals that enable them to recognize complex visual patterns, such as flowers and human faces, with high accuracy. This finding reveals that even tiny brains, evolved over millions of years, can perform sophisticated computations by integrating movement and sensory input, challenging assumptions about brain size and intelligence. The study builds on previous work by the same team, moving from observing bee flight behavior to uncovering the neural mechanisms behind active vision. Their model demonstrates that intelligence arises from the interaction between brain, body, and environment, rather than from brain size alone. Supporting this, Professor Lars Chittka highlighted that insect microbrains require surprisingly few neurons to accomplish complex visual discrimination tasks, including face recognition. Published in eLife and conducted in collaboration with Queen Mary University of London, this research
roboticsartificial-intelligencebee-brainpattern-recognitionneural-computationactive-visionbio-inspired-roboticsFlexible soft robot arm moves with light — no wires or chips inside
Engineers at Rice University have developed a flexible, octopus-inspired soft robotic arm that operates entirely through light beams, eliminating the need for wires or internal electronics. This innovative arm is powered by a light-responsive polymer called azobenzene liquid crystal elastomer, which contracts when exposed to blue laser light and relaxes in the dark, enabling precise bending motions. The arm’s movement mimics natural behaviors, such as a flower stem bending toward sunlight, allowing it to perform complex tasks like obstacle navigation and hitting a ball with accuracy. The control system uses a spatial light modulator to split a laser into multiple adjustable beamlets, each targeting different parts of the arm to flex or contract as needed. Machine learning, specifically a convolutional neural network trained on various light patterns and corresponding arm movements, enables real-time, automated control of the arm’s fluid motions. Although the current prototype operates in two dimensions, the researchers aim to develop three-dimensional versions with additional sensors, potentially benefiting applications ranging from implantable surgical devices to industrial robots handling soft materials. This approach promises robots with far greater flexibility and degrees of freedom than traditional rigid-jointed machines.
soft-roboticslight-responsive-materialsazobenzene-liquid-crystal-elastomermachine-learningflexible-robot-armremote-control-roboticsbio-inspired-robotics