Tech
New AI System Helps “Kidnapped” Robots Find Their Way in Changing Environments
Researchers in Spain have developed an AI system that allows robots to recover their position even after being moved, powered off, or displaced, offering a solution to the long-standing “kidnapped robot” problem. The system, designed at Miguel Hernández University of Elche, could enable autonomous machines to navigate safely in environments that change over time.
Autonomous robots, used in service operations, logistics, infrastructure inspection, environmental monitoring, and self-driving vehicles, often rely on satellite navigation systems such as GPS. These signals can be unreliable near tall buildings or completely unavailable indoors, making precise localisation a persistent challenge.
The new approach, called MCL-DLF (Monte Carlo Localisation – Deep Local Feature), uses 3D LiDAR technology to scan surroundings with laser pulses, creating a detailed map-like representation of the environment. By analysing both large structures and small distinguishing details, the system helps robots determine their exact location.
“This is similar to how people first recognise a general area and then rely on small distinguishing details to determine their precise location,” said Míriam Máximo, lead author of the study and a researcher at Miguel Hernández University of Elche.
MCL-DLF uses AI to identify which environmental features are most useful for localisation. The system maintains multiple possible location estimates simultaneously and continuously updates them as new sensor data becomes available. This allows robots to maintain reliable positioning even when environments look similar or have changed, such as when vegetation shifts or lighting conditions vary.
The research team tested the system over several months on the university campus under diverse conditions, including different seasons, lighting, and natural changes in vegetation. Results showed that MCL-DLF provided stronger positioning accuracy and more consistent performance compared with conventional localisation methods.
By enabling robots to navigate without constant reliance on external infrastructure, the system could increase operational independence in real-world environments, where conditions rarely remain static. Reliable localisation is particularly important for tasks where safety and precision are critical, such as autonomous deliveries, environmental monitoring, and industrial inspections.
The development of MCL-DLF represents a significant advance in robotics, providing a practical solution to the kidnapped robot problem. Researchers say the technology could help service and industrial robots operate more effectively in complex, dynamic settings, paving the way for wider adoption of autonomous systems in both indoor and outdoor environments.
With AI-driven localisation, robots may soon be able to recover from displacements quickly and continue tasks without human intervention, making them more resilient and adaptable in everyday operations.
Tech
Meta Apps Collect More User Data Than Other Big Tech, Study Finds
Meta’s apps collect more types of user data on average than applications from other major technology companies, according to research by cybersecurity and privacy company Surfshark.
The study analysed 171 apps available through Apple’s App Store that were developed by Meta, Google, Microsoft, Apple and Amazon. Researchers examined the types of information identified in each app’s privacy disclosures across 35 data categories.
Meta ranked highest, with its apps collecting an average of 25 out of 35 possible data types. Surfshark said this was more than three times the average recorded for some other major technology companies.
The categories examined included information such as browsing history, precise location, purchase details and other personal data that applications can collect or associate with users.
Seven of the apps identified among the most data-intensive were owned by Meta. They included Facebook, Messenger and Meta AI, as well as Meta Horizon, Meta Ads Manager, Meta Business Suite and Forum.
The findings put Meta well ahead of the other companies included in the analysis.
Google apps collected an average of 17 data types, according to Surfshark, while Amazon collected 12. Microsoft averaged eight types and Apple seven.
The research focuses on the number of data categories associated with each application rather than the volume of individual records collected from users. It also reflects information disclosed by developers through App Store privacy labels, which can vary according to how companies classify and report their data practices.
Meta operates some of the world’s most widely used digital platforms, including Facebook, Instagram, Messenger and WhatsApp. Its services rely heavily on advertising and personalised experiences, making user information an important part of its business model.
The findings are likely to renew questions about how much personal information consumers share when using popular social media and technology services.
Privacy concerns have increased as technology companies expand their use of artificial intelligence, targeted advertising and personalised recommendations. AI-powered applications can require access to additional information depending on their features and how users interact with them.
Surfshark’s analysis does not by itself establish whether any company has violated privacy laws or whether the data collected is used improperly. The number of data categories listed by an app also does not necessarily indicate how much information a particular user contributes.
However, the research highlights the differences in data collection practices among major technology companies.
The findings could encourage users to review application privacy settings and the information requested by apps before installing or continuing to use them.
As regulators and consumers place greater scrutiny on digital privacy, technology companies face increasing pressure to explain clearly what information they collect, why it is needed and how long it is retained.
Tech
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