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About

Martin Rufli is a robotics researcher whose work sits at the intersection of multi-robot coordination, motion planning, and autonomous navigation. He is perhaps best known for his foundational contributions to reciprocal collision avoidance, particularly for non-holonomic robots — systems constrained in how they can move, much like wheeled vehicles. His 2012 paper on optimal reciprocal collision avoidance has accumulated 278 citations, establishing him as a key voice in decentralized multi-robot motion planning. Building on this, his 2013 work introduced the continuous control obstacle framework, advancing how robots maintain smooth, physically consistent trajectories while avoiding one another in real time. Beyond safety-critical navigation, Rufli has explored the creative potential of robot swarms, leading projects in which groups of mobile robots form artistic patterns and even function as dynamic, reconfigurable pixel displays. These visually striking systems, earning over 100 and 77 citations respectively, demonstrate both algorithmic elegance and public engagement with robotics. His earlier work on lattice graph design and smooth path planning in cluttered indoor environments further reflects a consistent emphasis on practical, deployable solutions. Across his career, Rufli has shown a rare ability to bridge rigorous theoretical contributions with imaginative, real-world robotic applications.