Researchers developed MASTER, a distributed optimization method that addresses the challenge of matching measurements from different sensors in cooperative localization. The method produces smaller localization errors than existing methods, especially with more sensors participating, and works with various sensing data types.
Researchers at FAU's CA-AI are developing machine learning technologies to enable autonomous systems to sense, learn, and act in coordination. The project aims to overcome the challenge of individual smart systems failing to collaborate effectively as part of a network.
A team of scientists has developed a novel navigation strategy inspired by honeybees, allowing small robots to travel far away from home and return successfully. The 'Bee-Nav' system uses a neural network to process panoramic images of the environment, enabling lightweight, safe robots to navigate on their own.
Researchers at Penn and UMich created microscopic swimming machines that can independently sense and respond to their surroundings, operate for months, and cost just a penny each. The robots are powered by light and can be programmed to move in complex patterns, sense local temperatures, and adjust their paths accordingly.
Scientists from Japan developed a theoretical framework that explains how collective cells can perform complex tasks. The key is distributed information processing and reinforcement learning in the environment.
Researchers propose that neural activity in each animal becomes linked through perception, allowing individuals to maintain bearings towards others relative to stable features in the environment. This synchronization gives rise to spontaneous alignment and collective movement.
A NJIT student-faculty team won a best presentation award for their research on simulating ant swarm aggregations dynamics. Their study showed that ant swarms exhibit fluid and elastic properties, similar to biological systems.
Researchers developed T-STAR system, enabling swarms of drones to fly faster and safer in complex environments. The technology has potential for various applications, including search and rescue, disaster response, and logistics.
A team of researchers at Princeton University and Harvard found that fish schools do not form diamond shapes as previously believed. Instead, they adopt a dynamic ladder pattern, allowing them to stagger in multiple planes and reduce energy expenditure.
Scientists used virtual reality to mimic schooling behavior in zebrafish, discovering a simple and robust control law that enables coordinated motion. This natural algorithm was then applied to swarms of robotic cars, drones, and boats, achieving performance comparable to state-of-the-art autonomous systems.
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.
A team of researchers has created a robotic material-like collective that can change shape and stiffness in response to internal signals. The robots, composed of disk-shaped autonomous units, use light sensors, magnets, and force fluctuations to achieve this behavior, reducing power consumption compared to traditional robotic systems.
Researchers at MIT have developed a new design for robotic insects that can perform precise pollination with increased speed and maneuverability. The revamped robot has a longer flight duration and can complete acrobatic maneuvers, enabling it to aid in mechanical pollination and boost fruit yields.
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.
Scientists have developed swarms of tiny magnetic robots that can lift and transport heavy objects, thanks to their unique assembly configuration and rotating magnetic field. The microrobots can even guide small organisms through complex motions.
Physicists from the University of Konstanz have created a solution using microrobots and counterfactual rewards to ensure fair distribution of load in collective tasks. The approach enhances efficiency and provides insights into improving teamwork in various collective systems.
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.
A team of Würzburg researchers is using a swarm of autonomous robots to explore the Martian canyon system, Valles Marineris. The robots will collect data on the canyon's geology and search for signs of liquid water and potentially life, shedding light on the planet's habitability.
The WVU team, led by Yu Gu, is testing Loopy's ability to 'co-design' itself and learn to mark contaminated areas. Inspired by natural phenomena like ant swarms and tree roots, Loopy changes form in response to its environment.
The EMERGE project aims to demonstrate a new framework for coordinating artificial systems and humans. Shared awareness enables simpler AI systems to work together effectively, reducing energy costs and increasing efficiency.
Eötvös Loránd University researchers develop first large-scale autonomous drone traffic solution, combining route planning and bio-inspired flocking models to avoid conflicts and manage remaining issues. The system can handle up to 5000 drones in two dimensions with varying speeds and priorities.
Researchers developed a computational model to understand key decision-making processes, showing how group rewards improve performance. A videogame model inferred sequences of decisions from visual data, revealing the importance of balancing exploration and resource usage in collective behavior.
The team aims to create a system that can deliver items without human contact, using cables, knots, and multiple robots. They will focus on scaling up the transport of small objects like a basketball and solar panel.
Researchers at Princeton University and North Carolina State University have combined ancient paperfolding and modern materials science to create a soft robot that can bend and twist through mazes with ease. The new design allows the flexible robot to crawl forward and reverse, pick up cargo and assemble into longer formations.
The WVU Research Experience for Undergraduates program aims to solve real-world problems in Appalachia using mobile robotics. Students will conduct independent research in areas like drone navigation and swarming behaviors, focusing on enabling change with robotics tools.
By replicating nature's swarm behavior, researchers have created 'smart swarms' of microscopic robots that can adapt to changing environments, leading to improved task performance. This breakthrough enables potential applications in autonomous drone fleets, efficient drug delivery, and cleaning contaminated water.
Researchers have created MilliMobile, a tiny, self-driving robot powered only by surrounding light or radio waves. Equipped with sensors and tiny computing chips, it can move indefinitely on harvested power, enabling new abilities for swarms of robots in areas where other sensors struggle to generate nuanced data.
Researchers aim to access ocean waters hidden beneath ice shelves, where critical information about climate change is stored. An intelligent mothership and coordinated marine robots will communicate data from under-ice cavities, optimizing sampling and configuration.
The UW team's system uses self-deploying microphones to divide rooms into speech zones and track the positions of individual speakers. The robots disperse as far from each other as possible, allowing for greater sound control and isolating specific areas or separating simultaneous conversations.
Researchers proposed a novel self-organizing approach for robot swarms to achieve consensus, combining aspects of centralized and decentralized control. This method reduces uncertainty sources and enhances collective perception accuracy, enabling robots to fuse sensor information without global or static communication networks.
Tubificine worms can form entangled blobs that behave as a single organism to adapt to extreme environments and migrate more efficiently. Researchers successfully simulated collective movements of worm blobs in confined terrain, facilitating design of future swarm robotic systems.
Researchers demonstrate potential of blockchain technology in securing robot swarm coordination and neutralizing harmful Byzantine robots. By using a blockchain-based token economy, the swarm can identify and exclude malicious robots, ensuring autonomous and safe deployment.
Researchers at Brown University have developed a krill-inspired robot called Pleobot, which emulates the swimming method of krill to navigate complex marine environments. The platform has the potential to enable scientists to understand how to engineer better robots for ocean navigation and exploration.
The Mori3 robot is a polygon shape-shifting robot designed for space travel, capable of morphing into any 3D object and adapting to various environments. Its versatility makes it an ideal candidate for communication and external repairs in spacecraft.
Researchers at Cornell University have developed a method to control the behavior of swarming microrobots by varying their size. By mixing different sizes of microrobots, they can self-organize into diverse patterns that can be manipulated when a magnetic field is applied. This technique may help inform future applications such as targ...
Researchers develop microrobotic system using magnetic nanoparticles to target and eliminate fungal pathogens, achieving rapid eradication within 10 minutes. The nanozyme-based approach offers a potent and concentrated antifungal effect without affecting uninfected areas.
A team of researchers at Johannes Gutenberg University Mainz studied the collective behavior of small robots and found that they can solve tasks that a single machine cannot. The study uses statistical physics to analyze how the robots interact and move, revealing potential applications in medical and pharmaceutical applications.
Researchers at MIT have created a way for tiny robots to recover from severe damage to their wings, enabling them to sustain flight performance. The development uses laser repair methods and optimized artificial muscles that can isolate defects and overcome minor damage, allowing the robot to continue flying effectively.
Researchers at the University of Arizona found that rock ants follow a methodical search strategy, combining systematized meandering with random movement to efficiently explore new areas. This unique behavior may provide insights into the evolution of exploration strategies in other species.
Scientists studied African cichlid fish's lateral line sensing organ to understand cues for collective behavior, which could aid underwater robots. They found that different areas of the lateral line influence swimming patterns in shoals.
Researchers at Leipzig University developed an experimental model of microswimmers that exhibit properties of natural swarm intelligence. The swimmers' internal states and navigation rules can be controlled, allowing for the observation of complex collective behaviors.
Researchers have made significant progress toward creating robots that can build nearly anything, including vehicles, buildings, and even bigger robots. The new system uses complex voxels that can carry power, data, and force, enabling the building of structures with intelligence.
Researchers have developed a new miniature robot called Joey that can explore real pipe networks completely on its own, weighing just 70g. The robots are small enough to fit in the palm of your hand and are equipped with energy-efficient sensors to navigate through narrow sections and obstacles.
Researchers have developed a novel global-to-local design approach to compose heterogeneous swarms of robots, enabling them to achieve collective behavior. The system allows users to define target behaviors by changing the number and position of distribution's modes, enabling swarms to adapt autonomously.
Dr. Cameron Nowzari's project aims to challenge current methods for designing swarm systems and proposes a new framework combining mathematical methods, cybernetics, and social sciences. Funding from the Office of Naval Research will support this ambitious endeavor until late 2025.
Researchers used microrobots to demonstrate how a swarm of animals can complete an optimum flight response even if individual animals do not notice the threat or they react incorrectly. The study suggests that missing information from individual members can be compensated by other members, which may explain why animals organize themsel...
Rescuers can locate avalanche victims four times faster using a robotic swarm search technique. Researchers at Sandia National Laboratories developed a computer program that enables humans to find buried skiers using mini-robots and simple radio equipment.