Robots cut 30% travel time using human-like memory in smart factories

Researchers at South Korea’s Daegu Gyeongbuk Institute of Science and Technology (DGIST) have developed a new “Physical AI” technology that enhances the navigation efficiency of autonomous mobile robots (AMRs) in environments such as logistics centers and smart factories. This technology mimics human-like memory by modeling the social phenomenon of spreading and forgetting information, enabling robots to distinguish between relevant, real-time obstacles and outdated, unnecessary data. By forgetting obsolete information—such as obstacles that have been cleared—the robots avoid unnecessary detours, improving movement efficiency and productivity in complex, dynamic settings. Testing in a simulated logistics center demonstrated significant performance improvements, with average travel times reduced by up to 30.1% and task throughput increased by 18.0% compared to conventional ROS 2 navigation systems. The technology requires only 2D LiDAR sensors, making it cost-effective and easy to integrate as a plugin into existing ROS 2 navigation stacks without hardware modifications. Beyond industrial applications, this approach holds promise