Dayi Dong

Yale University, University of California, Berkeley

Papers

Total Citations

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About

Dayi Dong is a robotics researcher whose work focuses on autonomous search and exploration, with a particular emphasis on ergodic coverage—a method that ensures robots thoroughly explore an environment by spending time proportional to the information density in each region. Dong’s major contributions lie in advancing ergodic search to be both time-optimal and safety-critical. In their highly cited 2023 paper “Time Optimal Ergodic Search” (16 citations), Dong introduced a framework that balances search thoroughness with time efficiency, a critical capability for time-sensitive applications like search and rescue. That same year, in “Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions” (15 citations), Dong addressed the challenge of guaranteeing collision-free trajectories in constrained spaces, integrating control barrier functions to ensure safety during autonomous exploration. More recently, Dong’s 2024 work on time-optimal ergodic search further refined multiscale coverage in minimum time, while their 2025 paper extended ergodic exploration to meshable surfaces, broadening the applicability to complex, real-world terrains. With a growing citation record and a clear trajectory of innovation, Dayi Dong is shaping the future of autonomous search and rescue robotics.