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UAE Begins Mapping Air Corridors for Air Taxis and Cargo Drones to Transform Urban Transportation

TII ·

The UAE has begun mapping air corridors and developing regulations for air taxis and cargo drones, aiming to transform urban transportation. The GCAA and ATRC entities (TII and ASPIRE) are collaborating to define aerial corridors within 20 months. These routes will connect key airports and locations, integrating piloted and autonomous vehicles. Why it matters: The initiative positions the UAE as a leader in advanced air mobility, potentially easing congestion and setting a global benchmark for future urban mobility.

Technology Innovation Institute and Resource Industries Partner to Advance Autonomous Systems for Intelligence-Led Airpower

TII ·

The Technology Innovation Institute (TII) and Resource Industries have partnered to integrate autonomous air system technologies into operational defense platforms. The collaboration will embed TII's research, including synthetic aperture radar (SAR) and radio frequency (RF) magnetic mapping, into Resource Industries' aerial platforms. The aim is to enhance intelligence gathering and decision-making in complex environments. Why it matters: This partnership signifies the UAE's commitment to advancing its defense capabilities through indigenous innovation and autonomous systems, aligning with national strategies for technological advancement.

Learning Robot Super Autonomy

MBZUAI ·

Giuseppe Loianno from NYU presented research on creating "Super Autonomous" robots (USARC) that are Unmanned, Small, Agile, Resilient, and Collaborative. The research focuses on learning models, control, and navigation policies for single and collaborative robots operating in challenging environments. The talk highlighted the potential of these robots in logistics, reconnaissance, and other time-sensitive tasks. Why it matters: This points to growing research interest in advanced robotics in the region, especially given the focus on smart cities and automation.

A Decentralized Multi-Agent Unmanned Aerial System to Search, Pick Up, and Relocate Objects

arXiv ·

This paper presents a decentralized multi-agent unmanned aerial system designed for search, pickup, and relocation of objects. The system integrates multi-agent aerial exploration, object detection/tracking, and aerial gripping. The decentralized system uses global state estimation, reactive collision avoidance, and sweep planning for exploration. Why it matters: The system's successful deployment in demonstrations and competitions like MBZIRC highlights the potential of integrated robotic solutions for complex tasks such as search and rescue in the region.

UAE Launches Next-Gen GPS-Less Navigation and Secure Flight Control to Strengthen Aviation Security

TII ·

ADASI has adopted VentureOne's Perceptra, a GPS-less navigation technology, and Saluki, a high-security flight control technology, both developed by the Technology Innovation Institute (TII). These technologies enhance resilience, precision, and security for autonomous aerial operations, addressing vulnerabilities in GPS-dependent systems. The agreement was formalized at IDEX 2025. Why it matters: This deployment of advanced autonomous flight technologies in the UAE strengthens aviation security and positions the region as a leader in resilient, GPS-independent navigation solutions.

XXXIV General Assembly and Scientific Symposium (GASS) of the International Union of Radio Science

TII ·

The 34th General Assembly and Scientific Symposium (GASS) of the International Union of Radio Science (URSI) will be held in Rome from August 28 to September 4. The Technology Innovation Institute’s Directed Energy Research Center (DERC), led by Dr Chaouki Kasmi, will present a tutorial and five scientific papers. DERC's presentations will focus on advances in electromagnetics and optoelectronics. Why it matters: DERC's participation highlights the UAE's growing role in international radio science research and development.

Upsampling Autoencoder for Self-Supervised Point Cloud Learning

arXiv ·

This paper introduces a self-supervised learning method for point cloud analysis using an upsampling autoencoder (UAE). The model uses subsampling and an encoder-decoder architecture to reconstruct the original point cloud, learning both semantic and geometric information. Experiments show the UAE outperforms existing methods in shape classification, part segmentation, and point cloud upsampling tasks.