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IIT Guwahati Startup Develops AI-Powered Robots for Border Surveillance

A startup from IIT Guwahati has created AI-powered robots specifically designed for real-time surveillance along international borders. The Indian Army is currently evaluating this system for possible use in sensitive regions.

Developed by Da Spatio Rhobotique Laboratory Pvt Ltd (DSRL), these robots are intended to improve security by offering continuous monitoring in difficult environments. Unlike traditional surveillance techniques that depend on fixed cameras, drones, and manual patrols, these autonomous robots can navigate obstacles, adapt to various terrains, and function around the clock. Arnab Kumar Barman, CEO of DSRL, states that the system incorporates multiple sensors for intelligence collection and threat identification.

“The robotic surveillance system addresses key challenges such as endurance, adverse weather conditions, and terrain constraints,” he said.

The Defence Research and Development Organisation (DRDO) has recognized the technology’s promise for enhancing India’s defence capabilities. The Indian Army is currently performing field tests to assess its performance. If these trials prove successful, the system may be utilized for border security, monitoring vital infrastructure, and overseeing military installations. Keyur Sorathia, the Head of the Technology Incubation Centre at IIT Guwahati, highlighted the importance of this advancement.

“The Indian Army’s trials indicate a step towards large-scale deployment of indigenous, AI-driven defence solutions,” he said.

As threats like rogue drones and infiltration attempts rise, the role of AI-powered autonomous systems is becoming crucial in defense strategies. The innovation from the IIT Guwahati-supported startup supports India’s larger objective of bolstering security with homegrown technology. As field trials advance, the possible use of these surveillance robots may transform India’s border security strategy, decreasing dependence on traditional surveillance methods and improving real-time threat detection.

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