In a groundbreaking advancement poised to reshape the landscape of autonomous robotics, a research group at the Daegu Gyeongbuk Institute of Science and Technology (DGIST) has unveiled a novel Physical AI technology that significantly boosts the efficiency of multi-robot navigation. Led by Professor Kyung-Joon Park from the Department of Electrical Engineering and Computer Science and the Physical AI Center at DGIST, this innovative system mimics fundamental social dynamics—particularly the phenomena of information spread and gradual forgetting—to enhance cooperative navigation tasks among Autonomous Mobile Robots (AMRs) in complex industrial environments.
Autonomous Mobile Robots have become integral to the automation of logistics centers, manufacturing floors, and large-scale warehouse operations, performing vital roles in material transport and inventory management. Despite their increasing prominence, these robots often face significant challenges as they navigate dynamic environments riddled with unpredictable obstacles such as forklifts, work lifts, or mispositioned cargo. Traditional navigation algorithms primarily enable these robots to react to immediate, localized conditions. This reactive approach, however, frequently results in suboptimal routing, unnecessary detours, and consequentially, diminished productivity and throughput.
Professor Park’s team recognized this bottleneck and turned to an unexpected source for inspiration: human societal behaviors. Humans excel at efficiently disseminating critical information while simultaneously discarding irrelevant or outdated details, a process vital to maintaining effective collective awareness. Harnessing this concept, the researchers devised a mathematical model replicating the rapid diffusion and subsequent fading of social issues. Integrating this model into a collective intelligence framework allowed the AMRs to selectively share pertinent spatial and environmental data, dynamically filtering out redundant or obsolete obstacle information.
The ramifications of this approach are profound. By emulating social forgetting, the robots avoid information overload and focus computational resources on salient data. This results in a more fluid inter-robot communication system inherently capable of prioritizing urgency and relevance. When applied to navigation, such selective information processing translates into smoother, more coordinated path planning and obstacle avoidance, fundamentally advancing the cooperative capabilities of the robotic fleet.
To validate their system, the research team conducted extensive simulations within the Gazebo environment, a high-fidelity robotics simulation platform that faithfully replicates the visual, physical, and sensor complexities of a real-world logistics center. The results were compelling: the Physical AI-driven navigation framework improved task throughput by up to 18%, while simultaneously slashing average driving times by over 30% compared to conventional ROS 2 navigation stacks. These metrics underscore the technology’s capacity to not merely avoid obstacles but intelligently interpret and anticipate environmental dynamics akin to social cognition in humans.
The elegance of this innovation lies not only in its performance gains but also in its pragmatic adaptability. The system operates exclusively with 2D LiDAR sensors, foregoing the need for costly or bulky supplementary sensory arrays. By encapsulating the algorithmic enhancements within a plugin compatible with the widely adopted ROS 2 navigation stack, the team has ensured seamless integration with extant industrial robotic platforms. This compatibility paves the way for rapid adoption across various sectors, including drone swarms, autonomous vehicles, and robotic logistics units, without necessitating extensive hardware overhauls.
Moreover, the implications of this technology extend beyond industrial automation. In the context of burgeoning smart city initiatives, this cooperative navigation methodology holds promise for orchestrating autonomous vehicle fleets and managing urban traffic flows more effectively. Similarly, applications in large-scale exploration and rescue operations stand to benefit from the robots’ enhanced situational awareness and streamlined communication, potentially improving mission success rates and operational safety in unpredictable environments.
Professor Kyung-Joon Park highlighted the broader significance of the work, emphasizing how embedding social learning principles within Physical AI represents a paradigm shift toward machines that do not simply react but exhibit nuanced, human-like behaviors. This evolution heralds a future where autonomous systems possess a deeper understanding of contextual importance, enabling them to navigate the intricacies of real-world settings with unprecedented autonomy and collaboration.
This research was undertaken with the active involvement of integrated master’s and doctoral students Jiyeong Chae and Sanghoon Lee, who served as the study’s first authors. Professor Park, also CTO of the Physical AI startup S innovations Co., Ltd., played a central role in translating theoretical insights into industrially viable solutions aimed at reducing entry barriers for AMR deployment. The project received support from the AI Fellowship program under the Ministry of Science and ICT, reflecting governmental backing for cutting-edge artificial intelligence research.
The team’s findings were shared with the international scientific community through publication in the Journal of Industrial Information Integration, an esteemed journal recognized within the top 2% of industrial engineering publications as classified by the Journal Citation Reports (JCR). This exposure ensures that the innovative concepts and technological breakthroughs emerging from DGIST contribute broadly to ongoing discourse and development within robotics and AI domains worldwide.
By converging insights from social dynamics and robotic navigation, this study marks a significant stride toward robots that are not mere mechanical automatons but sophisticated Physical AI entities capable of cooperative, intelligent decision-making. The potential ripple effects span numerous industries and societal applications, suggesting a future where autonomous systems harmoniously coexist with human environments, enhancing efficiency, safety, and adaptability in complex operational theaters.
Subject of Research:
Autonomous Mobile Robots (AMRs) navigation efficiency enhanced through social issue spreading and forgetting models integrated into Physical AI collective intelligence algorithms.
Article Title:
From issues to routes: A cooperative costmap with lifelong learning for Multi-AMR navigation
News Publication Date:
10-Sep-2025
Web References:
DOI link: http://dx.doi.org/10.1016/j.jii.2025.100941
References:
Published in the Journal of Industrial Information Integration
Keywords:
Autonomous robots, Physical AI, multi-robot navigation, cooperative navigation, lifelong learning, social dynamics in robots, ROS 2 navigation stack, 2D LiDAR robotics, logistics automation, smart factories, industrial robotics, robot collective intelligence
