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About

Andrew Short is a robotics researcher whose work spans motion planning, robotic automation, and legged locomotion. His most influential contribution, "Recent Progress on Sampling Based Dynamic Motion Planning Algorithms" (2016, 39 citations), provides a comprehensive review of probabilistic and sampling-based planners extended to dynamic environments — a foundational reference for researchers tackling high-degree-of-freedom robot motion problems. Short has also made significant strides in industrial robotics, developing automated offline programming systems that generate robot programs directly from CAD models, reducing the costly reprogramming burden that limits automation in low-volume manufacturing. This body of work, including his papers on automatic weld path generation and robotic welding programming (collectively garnering nearly 40 citations), addresses a practical barrier to widespread industrial adoption. His 2017 work on legged robot motion planning introduced Contact Dynamic Roadmaps (CDRM), advancing the ability of legged robots to navigate complex three-dimensional environments more efficiently. Further contributions in task-space motion planning decomposition demonstrate his broader commitment to making autonomous robot planning more tractable and generalizable. With over 100 cumulative citations, Short's research consistently bridges theoretical planning algorithms with real-world robotic applications.