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Compact AI training method reduces navigation conflicts in crowded environments

A research team led by Professor Daehee Park of the Department of Electrical Engineering and Computer Science at DGIST, in collaboration with a research team from KAIST, has developed a learning technique that enables a single compact AI model to simultaneously predict the movements of nearby people and plan safe navigation paths for robots while reducing performance degradation in both tasks. The research was presented at the 19th European Conference on Computer Vision (ECCV 2026) held in Malmö, Sweden, Sept. 8–12. The paper is available on the arXiv preprint server.

Flexible flight corridors let drones navigate cluttered spaces faster and more safely

Researchers at Durham University have developed a new drone navigation system that enables autonomous aircraft to fly faster, more smoothly and more safely through crowded and obstacle-filled environments. This leads to new possibilities for applications such as search and rescue, infrastructure inspection and environmental monitoring.