Eric Lanteigne
Papers
2
Total Citations
12
H-Index
2
About
Eric Lanteigne’s research lies at the intersection of biologically inspired robotics, path planning, and multi-robot coordination. He is best known for pioneering a biologically inspired node generation algorithm for hyper-redundant manipulators, which uses probabilistic roadmaps to solve complex path planning challenges—a foundational contribution that has garnered 9 citations since 2014. More recently, Lanteigne has advanced the field of multi-robot systems with a hybrid algorithm for optimized task allocation and coordination among specialized robots. His 2024 study introduces a novel combination of a deterministic greedy algorithm enhanced with a metaheuristic genetic algorithm, enabling even task distribution while minimizing global costs. This work addresses critical challenges in multi-factor task allocation, making it highly relevant for applications in manufacturing, search-and-rescue, and autonomous exploration. Lanteigne’s contributions demonstrate a consistent focus on bridging theoretical optimization with practical robotic systems, and his hybrid approach marks a significant step forward in scalable, efficient multi-robot coordination. With growing interest in autonomous systems, his research continues to influence both academic inquiry and real-world robotic deployments.
Research Focus
Key Achievements
Top Papers
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- 2