Andrew Messing
Papers
6
Total Citations
92
H-Index
4
About
Andrew Messing is a leading researcher in the algorithmic coordination of heterogeneous multi-robot teams, with a primary focus on task allocation, planning, and scheduling. His most influential work, "GRSTAPS" (35 citations), introduces a novel graphically recursive framework that simultaneously solves the interdependent problems of task planning, motion planning, task allocation, and scheduling—a critical advancement for deploying effective multi-robot systems in complex, real-world environments. Messing further advanced the field with his hierarchical planning approach for heterogeneous multi-robot routing problems (21 citations), which decomposes complexity by learning subteam performance. His research also addresses practical challenges, as demonstrated by his work on the Additive Manufacturing Excavator (16 citations), a landmark project at Oak Ridge National Laboratory that showcased 3D-printed large-scale metal parts at CONEXPO 2017. More recently, Messing has explored risk-tolerant coordination (2023), tackling the uncertainties inherent in real-world deployments. Through his interleaved approaches to trait-based task allocation and forward-chaining hierarchical planning, Messing has established himself as a key contributor to making multi-robot teams more adaptive, efficient, and robust.
Research Focus
Key Achievements
Top Papers
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- 4An Interleaved Approach to Trait-Based Task Allocation and Scheduling14 citations · 2021
- 5Forward Chaining Hierarchical Partial-Order Planning3 citations · 2021
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