China has deployed robotic snakes to inspect power lines and help ensure a stable electricity supply.

The snake-like robots wrap around power lines and use onboard cameras and sensors to detect hazards such as damaged wires, worn components, and abnormal temperatures.

According to online media reports, the robots have inspected more than 80 miles (130 kilometers) of distribution lines serving exam sites near airport no-fly zones.

The system is three times more efficient than manual inspections, reducing the need for labor-intensive, hazardous work while improving reliability and safety.

Robotic grid guardians

Power utilities in southwestern China are using robotic snakes to inspect power lines, showcasing a new approach to infrastructure monitoring in environments where drones face limitations.

The snake-like robots have been deployed in Kunming, the capital of Yunnan province, where they move directly along power lines while inspecting critical electrical infrastructure. Equipped with cameras and sensors, the robots can identify faults such as broken wires, worn components, and abnormal temperature changes that could signal potential failures, reports the South China Morning Post (SCMP).

Developed by the Guandu District Power Supply Bureau, the robotic system has already inspected more than 80 miles (130 kilometers) of power distribution lines. According to its developers, the technology delivers inspection efficiency three times higher than traditional manual methods.

Unlike aerial drones, which are widely used for power-line inspections, the robotic snakes operate directly on the cables they monitor. This allows them to work in locations where drone flights are restricted, including areas near airport no-fly zones. The robots are also less vulnerable to high-voltage electromagnetic interference, a common challenge that can disrupt drone control systems and reduce image quality.

Meet the snake-like #robot patrolling power lines in #Kunming.

Slithering along cables, it uses onboard cameras and sensors to spot damage and overheating in real time. Even better, it draws power straight from the line, enabling autonomous 24/7 monitoring without interruption. pic.twitter.com/zXJq61oft4

— China SCIO (@chinascio) June 10, 2026

The system addresses several limitations of drone-based inspections, including restricted flight zones, weather dependence, and limited battery endurance. By providing continuous close-range monitoring of power infrastructure, the robotic snakes enable utility crews to quickly identify and address potential hazards, reports SCMP.

Intelligent grid inspectors

Chinese power utilities are deploying a range of robotic systems, including snake robots, robot dogs, drones, and surveillance cameras, to monitor electricity infrastructure and maintain reliable power supplies during the national college entrance exams.

The snake robot features a flexible, multi-jointed body that allows it to move along power lines and navigate around obstacles such as insulators. This design enables it to reach areas that are difficult for human inspectors and aerial drones to access. A camera and onboard sensors mounted in the robot’s head detect potential faults and safety risks, while a non-contact power-harvesting system allows the robot to draw energy directly from the power lines it is inspecting.

Following successful trials on multiple transmission routes, utility operators have expanded the robot’s deployment to more complex sections of the power grid. Robot dogs are also being used for inspections in high-voltage environments and other locations that are challenging or hazardous for human workers. Combined with drones and fixed monitoring systems, the robotic platforms provide continuous oversight of critical electrical infrastructure.

The deployment comes as approximately 12.9 million students sat for China’s National Higher Education Entrance Examination, known as the gaokao, one of the country’s most important academic tests. Reliable electricity is considered critical during the examination period, prompting utilities to adopt advanced robotic technologies to improve inspection efficiency and reduce operational risks, reports SCMP.

A four-legged robot dog designed to detect toxic substances could soon become the first responder sent into dangerous areas and help firefighting crews assess the damage without risking their own safety.

The remote-controlled detection robot was developed by researchers at the Graz University of Technology (TU Graz) in Austria. It is a compact, quadruped system that also incorporates measurement devices already used by fire services.

According to the research team, the detection robot has already been extensively tested by emergency services as part of large-scale pollutant drills. It has proved its ability to provide real-time data from danger zones.

Gerald Steinbauer-Wagner, PhD, an associate professor and robotics researcher at TU Graz, revealed that the dog can be deployed on missions ahead of firefighting crews. “It measures pollutant concentrations and delivers the data together with a camera image in real time from the danger zone,” Steinbauer-Wagner explained.

First into danger

In chemical accidents, emergency crews need to identify hazardous substances quickly while operating under dangerous conditions. Firefighters must typically don bulky protective suits, such as a Level A or B Hazmat suit, prior to entering contaminated areas to take measurements.

Although necessary to ensure total isolation from hazardous environments, the process can consume valuable time during an emergency. To address this issue, the team turned to robotics.

As per the researchers, the robotic dog can be sent in before firefighters to collect critical information from the scene. “This allows the emergency services to assess the situation without having to enter the danger zone themselves,” Steinbauer-Wagner continued.

The technology reduces firefighting teams’ direct exposure to toxic substances. It could significantly improve their safety during industrial accidents and hazardous materials incidents.

“Based on a comprehensive requirements analysis, we developed an appropriate robotic strategy and integrated it into existing standard operational procedures,” Steinbauer-Wagner added. “This should ensure seamless collaboration between man and machine during the operation.”

A safer perspective

Instead of creating a new system, the team made it compatible with existing fire-service procedures. The researchers conducted an extensive requirements analysis before integrating it into established hazardous materials response protocols. The robot relies on commercially available parts for mobility, sensing, data processing, communication, and visualization.

“The results of the field tests were extremely promising in terms of mobility, user-friendliness and effectiveness,” Steinbauer-Wagner reported. Meanwhile, a recent potential analysis supported by the Austrian Research Promotion Agency (FFG) highlighted several advantages of the technology.

Gerald Czech of the Austrian Federal Fire Service Association emphasized that, in the event of a real incident, he would send the robot dog into the contaminated area before the firefighters go in. “For the fire service, every minute counts during a hazardous materials operation,” he said in a statement.

Czech further said receiving measurement data and images from the danger zone while crews are still putting on protective gear could save valuable time during an emergency. “This increases the safety of the crew and improves operational command,” he concluded.

Experts from the Styrian and Upper Austrian fire service associations, TU Graz, the University of Applied Sciences Upper Austria, as well as the Disaster Competence Network Austria, support the use of detection robots. They propose making them a permanent part of fire departments’ hazardous materials response teams.

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Google has unveiled DiffusionGemma, a new experimental AI model that generates text using diffusion rather than the autoregressive approach used by most large language models today. The company says the model can deliver up to four times faster text generation on dedicated GPUs while running on consumer hardware.

The model builds on Google’s Gemma 4 family and Gemini Diffusion research. Unlike traditional language models that generate text one token at a time from left to right, DiffusionGemma creates and refines blocks of text in parallel.

According to Google, the approach enables output speeds exceeding 1,000 tokens per second on an NVIDIA H100 GPU and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.

The company says DiffusionGemma is aimed at developers working on speed-sensitive applications such as interactive editing, rapid content iteration, code infilling, and other workflows where low latency is more important than maximum output quality.

Breaking text bottlenecks

Most large language models generate text sequentially, predicting one token after another. While effective, this process can leave local hardware underutilized when serving a single user.

DiffusionGemma takes a different approach. Instead of generating text word by word, it creates a 256-token block at once and then repeatedly refines it through multiple passes.

Google compares the difference to moving from a typewriter to a printing press. Rather than waiting for each token to be generated before producing the next one, the model processes an entire section of text simultaneously.

The company says this shifts the bottleneck from memory bandwidth to compute performance, allowing modern GPUs to operate more efficiently during local inference.

Another key feature is bi-directional attention. Since the model generates text in parallel, every token can attend to every other token during generation. This makes it better suited for tasks where future context matters, such as code completion, in-line editing, mathematical structures, and biological sequences.

Google highlighted a demonstration in which DiffusionGemma was fine-tuned to solve Sudoku puzzles, a task that can be challenging for conventional autoregressive models because later tokens influence earlier decisions.

Designed for local AI

The model uses a 26-billion-parameter mixture-of-experts architecture but activates only 3.8 billion parameters during inference. According to Google, this allows the model to fit within roughly 18 GB of VRAM when quantized, making it accessible on high-end consumer GPUs.

DiffusionGemma also includes an iterative self-correction mechanism. Because it evaluates an entire text block during refinement, it can identify and fix mistakes as generation progresses.

However, Google acknowledged that the model prioritizes speed over quality. The company said standard Gemma 4 models remain the preferred choice for production environments where output quality is the primary concern.

The speed advantage is also most apparent in local deployments and low-concurrency environments. In cloud settings serving large numbers of users simultaneously, conventional autoregressive models can often utilize hardware efficiently through batching, reducing the benefits of diffusion-based generation.

Google has released DiffusionGemma under an Apache 2.0 license through Hugging Face and is supporting deployment through tools including MLX, vLLM, Hugging Face Transformers, NVIDIA NeMo, and Unsloth.