Papers
14
Total Citations
212
H-Index
7
About
Bin Xin is a researcher whose work sits at the intersection of multi-robot systems, combinatorial optimization, and intelligent automation. His research is primarily focused on designing advanced algorithms to solve complex coordination and scheduling problems, with real-world applications spanning post-disaster relief, warehouse automation, and environmental monitoring. Xin's most significant contributions center on the multipoint dynamic aggregation (MPDA) problem — a challenging framework for coordinating heterogeneous robot teams on geographically distributed, time-sensitive tasks. His pioneering work in this area includes developing ant colony optimization approaches, genetic programming-based coordination strategies, and multi-objective evolutionary algorithms, collectively accumulating nearly 100 citations and establishing him as a leading voice in the field. His auction-based spanning tree coverage algorithm (A-STC, 39 citations) further demonstrates his ability to translate theoretical optimization into practical multi-robot motion planning. Beyond robotics coordination, Xin has contributed to flexible job shop scheduling, robotic mobile fulfillment systems, and indoor chemical source searching, reflecting a broad research vision that embraces both foundational theory and applied engineering. His 2023 survey on modular design automation for intelligent robots signals an expanding interest in end-to-end robot design automation. With over 190 cumulative citations, Xin's work has established meaningful influence across robotics, evolutionary computation, and operations research communities.
Research Focus
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