Cyrill Stachniss from the University of Bonn presented recent work on agricultural robotics and self-driving cars. The talk covered autonomous field robots and their ability to perceive, model, and predict future developments in complex farming environments. The presentation also included developments in supervised and unsupervised learning for autonomous car perception systems. Why it matters: This highlights the growing interest in robotics research at MBZUAI and the potential for AI to transform key sectors in the GCC region like agriculture and transportation.
KAUST is developing a robotic system for automated date palm harvesting, combining robotics and AI. The system uses robotic arms with visual sensors to identify and harvest dates, flowers, and tree structures. Field trials are scheduled for the 2025 harvest season, with full operational capability expected within three years. Why it matters: This innovation could transform Saudi Arabia's date farming industry, increasing yields, reducing labor risks, and positioning the country as a leader in agricultural technology.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.
MBZUAI's new robotics department, launched in August 2023, aims to develop AI-driven robotic solutions for key UAE sectors. Professor Dezhen Song, the inaugural Professor of Robotics, highlights the department's focus on the "brain side" of robotics, enhancing perception, navigation, and control. Specific applications include automated infrastructure inspection for the energy sector and AI-driven precision agriculture techniques to improve crop yields and resource efficiency. Why it matters: MBZUAI's robotics research will contribute to the UAE's strategic goals in energy, food security, and sustainable development.
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.
Researchers introduce TomFormer, a transformer-based model for accurate and early detection of tomato leaf diseases, with the goal of deployment on the Hello Stretch robot for real-time diagnosis. TomFormer combines a visual transformer and CNN, achieving state-of-the-art results on KUTomaDATA, PlantDoc, and PlantVillage datasets. KUTomaDATA was collected from a greenhouse in Abu Dhabi, UAE.
This paper presents the design and deployment of an autonomous unmanned ground vehicle (UGV) equipped with a robotic arm for urban firefighting. The UGV uses on-board sensors for navigation and a thermal camera for fire source identification, with a custom pump for fire suppression. The system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020, where it achieved the highest score among UGV solutions and contributed to winning first place. Why it matters: This demonstrates the potential of autonomous robotics in addressing complex and dangerous real-world challenges like urban firefighting in the GCC region and beyond.
This paper introduces a convolutional transformer model for classifying tomato maturity, along with a new UAE-sourced dataset, KUTomaData, for training segmentation and classification models. The model combines CNNs and transformers and was tested against two public datasets. Results showed state-of-the-art performance, outperforming existing methods by significant margins in mAP scores across all three datasets.