Bees, ants and termites don’t need blueprints. They may have queens, but none of these species breed architects or construction managers. Each insect worker, or drone, simply responds to cues like warmth or the presence or absence of building material. Unlike human manufacturing, the grand design emerges simply from the collective action of the drones—no central planning required.
Now, researchers at Penn Engineering have developed mathematical rules that allow virtual swarms of tiny robots to do the same. In computer simulations, the robots built honeycomb-like structures without ever following—or even being able to comprehend—a plan.
“Though what we have done is just a first step, it is a new strategy that could ultimately lead to a new paradigm in manufacturing,” says Jordan Raney, Associate Professor in Mechanical Engineering and Applied Mechanics (MEAM), and the co-senior author of a new paper in Science Advances. “Even 3D printers work step by step, resulting in what we call a brittle process. One simple mistake, like a clogged nozzle, ruins the entire process.”
You’re watching: 2025’s VOLONAUT AIRBIKE – The Jet-Powered Flying Bike That’s Actually Real!
Forget sci-fi… this is the future happening right now. The Volonaut Airbike isn’t just a concept or a CGI teaser — it’s a real, jet-powered flying bike that’s already tearing through the skies in 2025!
Unlike bulky drones with spinning blades, this beast lifts off with raw jet propulsion — no exposed rotors, no cockpit, and no nonsense. It’s built from carbon fiber and 3D-printed parts, making it ultra-light — 7x lighter than a motorcycle. The rider becomes part of the machine, steering it by body movement while a smart onboard flight computer keeps everything stable.
Created by Tomasz Patan, the genius behind Jetson ONE, the Volonaut Airbike is capable of reaching speeds up to 200 km/h (124 mph), soaring over forests, cliffs, and even deserts with mind-blowing agility.
As artificial intelligence and smart devices continue to evolve, machine vision is taking an increasingly pivotal role as a key enabler of modern technologies. Unfortunately, despite much progress, machine vision systems still face a major problem: Processing the enormous amounts of visual data generated every second requires substantial power, storage, and computational resources. This limitation makes it difficult to deploy visual recognition capabilities in edge devices, such as smartphones, drones, or autonomous vehicles.
Interestingly, the human visual system offers a compelling alternative model. Unlike conventional machine vision systems that have to capture and process every detail, our eyes and brain selectively filter information, allowing for higher efficiency in visual processing while consuming minimal power.
Neuromorphic computing, which mimics the structure and function of biological neural systems, has thus emerged as a promising approach to overcome existing hurdles in computer vision. However, two major challenges have persisted. The first is achieving color recognition comparable to human vision, whereas the second is eliminating the need for external power sources to minimize energy consumption.
An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.
To help such a drone stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.
The study is published on the arXiv preprint server.
Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications…
The world’s largest drone “mothership” is getting ready for deployment in June. It’s designed to carry and launch up to 100 drones in a swarm, including kamikaze drones.
ASILAB is excited to introduce Asinoid – the world’s first true artificial superintelligence built on the architecture of the human brain. Designed to think, learn, and evolve autonomously like a living organism.
Asinoid isn’t just another AI. Unlike today’s pre-trained, prompt-driven models and agents, Asinoid is a self-improving and proactive mind. It learns over time. It remembers. It sets its own goals. And it gets smarter by rewiring itself from within.
An Asinoid can power a fleet of autonomous drones. Act as the brain inside your security system. It can drive your R&D, run your meetings, become the cognitive layer behind your SaaS product or even co-found a company with you.
The possibilities are endless. And we want to explore them with you.
We’re opening access to pioneering companies, researchers, and developers who want to build with us. If you’re ready to create something groundbreaking, let’s get started.
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