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About

Daniel Claes is a leading researcher in multi-robot systems, with a primary focus on decentralized coordination, collision avoidance, and bio-inspired robotics. His most influential work tackles the fundamental challenge of safe navigation under uncertainty, where he pioneered the integration of the velocity obstacle paradigm with Adaptive Monte-Carlo Localization to enable robust collision avoidance without requiring perfect global positioning—a contribution that has garnered over 100 citations. Claes has also made significant strides in human-aware navigation, developing decentralized algorithms that allow robots to safely share workspaces with humans and dynamic obstacles. His research extends to practical applications like multi-robot warehouse commissioning, where he proposed scalable decentralized planning to overcome the limitations of centralized control in large teams. Notably, his work on OctoSLAM advanced 3D mapping for UAVs, enhancing situational awareness in indoor environments. Claes has also explored insect-inspired coordination, applying principles from honeybee foraging and ant coverage to multi-robot systems. With a career spanning over a decade and papers accumulating hundreds of citations, his contributions are foundational to the field of autonomous multi-robot coordination.