Scott Kiesel

Amazon (United States), University of New Hampshire at Manchester

Papers

Total Citations

246

H-Index

About

Scott Kiesel is a leading researcher in multi-agent coordination and robotics, whose work bridges the gap between theoretical planning algorithms and real-world deployment. His primary research areas include multi-agent path finding (MAPF), task assignment, and motion planning in continuous spaces. Kiesel’s most influential contribution is his 2019 paper on "Persistent and Robust Execution of MAPF Schedules in Warehouses" (129 citations), which addresses the critical challenge of keeping physical robots operational over long time horizons without collisions—a foundational problem for warehouse automation. He further advanced the field with his 2018 work on "Conflict-Based Search with Optimal Task Assignment" (91 citations), which integrates task allocation into collision-free path planning, extending the popular CBS framework to handle more complex, real-world logistics. More recently, his 2021 paper on "Abstraction-Guided Sampling for Motion Planning" (26 citations) introduces novel techniques for efficiently navigating continuous, high-dimensional spaces by combining heuristic search with sampling-based methods like RRTs. Kiesel’s work is notable for its direct impact on industrial robotics, demonstrating how rigorous algorithmic solutions can be adapted for persistent, robust operation in dynamic environments.