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About

Kai Di is a robotics researcher whose work focuses on the critical challenge of enabling multi-robot systems to operate intelligently and safely in complex, real-world environments. His primary research areas include multi-robot task allocation (MRTA), foraging in hazardous settings, and the application of hyper-heuristic algorithms. Di’s major contributions lie in redefining traditional MRTA problems to account for "functional tasks" and "hidden workloads," moving beyond simplistic assumptions of minimizing time cost. His 2023 paper, "Solving the Multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm," has garnered 28 citations, highlighting its impact. He has also pioneered "risk-aware collection strategies" for multirobot foraging in dangerous environments like earthquake rescue, addressing the realistic threat of hazards that previous studies often ignored. His work on adversarial environments, including strategies for individual robots to respond to potential adversaries during battlefield rescue, further showcases his commitment to practical, robust solutions. Through his innovative use of model-based hyper-heuristic strategies, Kai Di is advancing the frontier of autonomous robotics, making multi-robot teams more adaptable, efficient, and resilient in the face of real-world uncertainty.