Papers
1
Total Citations
25
H-Index
1
About
Xinlin Song is a researcher specializing in multi-robot systems, path planning, and multi-objective optimization. Their most-cited work, "Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective" (2023), has garnered 25 citations, marking a significant contribution to the field of swarm robotics. Song’s research focuses on enhancing ant colony optimization algorithms to address complex coordination challenges, enabling efficient navigation for both individual robots and collaborative multi-robot teams. By integrating multi-objective criteria—such as minimizing path length, energy consumption, and collision risks—their work provides practical solutions for real-world applications like warehouse automation and search-and-rescue missions. This achievement underscores Song’s ability to bridge theoretical algorithm improvements with tangible robotic systems, offering a scalable framework for autonomous decision-making. Their contributions are particularly valuable for students and researchers exploring bio-inspired computing and distributed robotics, as they demonstrate how nature-inspired heuristics can solve intricate engineering problems. Song’s ongoing work continues to advance the efficiency and adaptability of robotic teams, solidifying their role as an emerging voice in intelligent systems research.
Research Focus
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Top Papers
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