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Category: drones

Giving drones a sense of ‘pain’ could help them predict instability before it happens

Imagine you’re running and you sprain your ankle. The pain makes you gingerly limp the rest of the way home. This is a great example of how nature adapts to failures in a system. The pain tells you: “If you continue running like normal, the injury will only get worse.” So you naturally adjust the way you run. Drones currently cannot do this with a worn-out propeller.

Researchers from Delft University of Technology and Wageningen University & Research have now demonstrated that a concept we learned from nature, which was originally developed to predict collapse in ecosystems, can also help detect when engineered systems are heading toward failure. This is crucial for ensuring drone and autonomous vehicle safety as they increasingly become part of everyday life.

“You can compare our approach to the way humans experience pain. After an injury, pain provides immediate feedback about our condition and helps us judge what actions remain safe,” says Jasper van Beers, a researcher at Delft University of Technology. “Machines generally lack this form of self-awareness. The new indicators, derived from real-time measurement data, offer a first step toward giving engineered systems a similar ability to recognize when they are approaching their limits.”

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3,970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

Keywords: SpiNNaker; artificial neural network; natural language processing; neuromorphic computing; sentiment analysis; spiking neural networks.

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AI isn’t a dual-use technology, it is inherently violent

When the Pentagon branded Anthropic CEO Dario Amodei “a liar with a god complex” over fears that his company’s AI could be used for weapons and surveillance, it exposed a deeper truth: the boundary between civilian and military technology no longer exists. The same systems that power translation, logistics, and digital assistants can just as easily identify targets or manipulate populations. Thomas Christian Bächle and Jascha Bareis argue that today’s AI is not simply “dual use” — it is inherently violent in design. Adaptive, autonomous, and globally networked, these machines fuse daily life with geopolitics, making peace itself a fading abstraction.

Drones have become an uncanny threat—not least in the wake of the cost of human life and the degrees of suffering and destruction they have inflicted in Russia’s war on Ukraine. In many European countries they have been sighted near critical infrastructure or military sites, either used for reconnaissance or sabotage, at times causing major disruptions in civilian air travel. Drones unsettle a population that is fearful and weary of the brutality of war at their doorstep. They have become a major element to what is labelled hybrid warfare, fought beyond the conventional ways of violence.

But this is not the whole picture. For years, drones have also been envisioned as a technology that bears the potential of bringing about major changes for the better: more efficient disaster relief, medical supply chains reaching even the remotest areas, optimized logistics or transportation. Drones also introduced a new visual – bird’s-eye-aesthetic of how to see the world.

China hits new milestone in space solar power project

XI’AN — Chinese scientists have taken a major step toward building a space solar power station, a giant power plant in space that could one day send energy back to Earth or to spacecraft.

A research team from Xidian University in Northwest China’s Shaanxi province has made significant progress on the Sun Chasing project, or “Zhuri” in Chinese. The team has developed a ground-based test system for wireless power transmission that can charge multiple moving targets at the same time.

In recent tests, the system achieved a wireless power transmission efficiency of 20.8 percent from direct current to direct current over a distance of 100 meters. It delivered 1,180 watts of power. The team has also built a wireless charging system for drones. In a test, a drone flying at 30 kilometers per hour was able to receive 143 watts of stable power from 30 meters away.

Jumping spiders inspire ultra-efficient 3D camera

This 3D camera estimates depth by comparing blur across two differently focused images of the same scene. The prototype generates real-time 3D maps while using less than a watt of power, sidestepping more energy-intensive approaches.

By borrowing a trick from tiny jumping spiders, Northwestern University engineers have developed an extremely energy-efficient 3D camera. Called SpiderCam, the new device senses depth the same way that jumping spiders judge distances before making a high-precision hop. To estimate depth, the system captures two images of the same scene with slightly different focus settings and measures subtle differences in blurriness between the two images.

With this strategy, the camera produces real-time 3D maps while consuming less than a watt of power. That’s less energy than used by a standard nightlight.

The innovation could enable a new generation of battery-powered devices that need to gauge their surroundings, like wearable technologies, assistive devices, robots and drones.

Nano Weapons: The Invisible Machines Changing Future Wars!

In this video, we explore the incredible and terrifying world of nano-weapons — microscopic machines designed for the battlefields of the future. From invisible drones to molecular-level assassins, nanotechnology is revolutionizing modern warfare in ways the world has never seen before. Discover how these tiny machines can spy, sabotage, and even kill at the atomic scale. We’ll uncover real-world research, secret military projects, and the ethical dangers behind the next generation of warfare. The rise of nano-weapons could change the balance of global power forever — but are we ready for what’s coming? Watch till the end to understand the full potential and risks of these microscopic war machines.
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Chris Hables Gray on AI and the Singularity: We Need Strong Citizenship!

In 2013, I interviewed a man who studies cyborgs and war for a living.

Somewhere in that conversation, Prof. Chris Hables Gray predicted a global pandemic. I chimed in that it would most likely stem from a bird flu outbreak.

We were both right. Neither of us wanted to be.

That was six years before COVID. And here we are in 2026, watching H5N1 headlines pile up again.

The point was never the prediction. The point was what he said we should do about it.

Chris did not pitch a gadget. He did not sell a forecast. He argued that surviving the century is not a technology problem; it is a citizenship problem.

Continue reading “Chris Hables Gray on AI and the Singularity: We Need Strong Citizenship!” | >

Open-source framework lets drones dodge obstacles in milliseconds while minimizing travel time

In the aftermath of a devastating earthquake, unpiloted aerial vehicles (UAVs) could fly through a collapsed building to map the scene, giving rescuers information they need to quickly reach survivors. But this remains an extremely challenging problem for an autonomous robot, which would need to swiftly adjust its trajectory to avoid sudden obstacles while staying on course.

Researchers from MIT and the University of Pennsylvania developed a new trajectory-planning system that tackles both challenges at once. Their technique enables a UAV to react to obstacles in milliseconds while staying on a smooth flight path that minimizes travel time.

Their system uses a new mathematical formulation that ensures the robot travels safely to its destination along a feasible path, and that is less computationally intensive than other techniques. In this way, it generates smoother trajectories faster than state-of-the-art methods.

Stanford CS231N Deep Learning for Computer Vision I 2025

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into deep learning methods with a focus on end-to-end models for core vision tasks, alongside modern approaches such as transformers, diffusion models, and visual-language models that power today’s AI systems. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. https://online.stanford.edu/courses/cs231n-deep-learning-computer-vision

New Drone Tech Helps Locate Accessible Water on Mars

Drone-mounted ground-penetrating radar can efficiently map subsurface ice depth, providing a vital tool for locating accessible water sources for future Mars missions. [ https://www.labroots.com/trending/technology/30506/drone-tec…ter-mars-2](https://www.labroots.com/trending/technology/30506/drone-tec…ter-mars-2)

How can drones help find buried water on Mars? This is what a recent study published in Journal of Geophysical Research: Planets hopes to address as a team of scientists investigated how ground-penetrating radar installed on drones could be used to find buried water ice on Mars. This study has the potential to help scientists develop new methods for helping future astronauts on Mars locate accessible resources, specifically water ice, which they can use for mission essential purposes.

For the study, the researchers used a DJI Matrice 600 Pro drone and a MALA Geodrone radar to search for buried water ice in Sourdough rock glacier (RG), Alaska, and Galena Creek RG, Wyoming with bulk glacier thicknesses of 28.5 meters (93.5 feet) and 48.6 meters (159.4 feet), respectively. The primary motivation for the study was to address a knowledge gap regarding orbital data and ground-level data for searching for water ice on Mars. This is because while Mars orbiters have found buried water ice on Mars, their radars are limited to 10–20 meters (32.8−65.6 feet) beneath the surface. In the end, the researchers compared their findings with previous data from drillings and ice cores and discovered a match, indicating their drone experiment to identify buried water ice worked.

“We are filling the gap between today’s orbital observations and a more distant future, where astronauts land on Mars and make observations on the ground,” said Roberto Aguilar, who is a PhD student at University of Arizona and lead author of the study. “This gives us a way to investigate the glaciers now, from the air.”

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