Adam Pawlowski

Papers

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About

Adam Pawlowski is a robotics researcher whose work focuses on the critical intersection of motor control, trajectory planning, and autonomous navigation. His primary contributions lie in developing novel algorithms that enhance the precision and efficiency of mobile robotic systems. Pawlowski is particularly known for his innovative application of meta-heuristic optimization, specifically the Grey Wolf Optimizer (GWO), to solve complex engineering challenges. In his most-cited work (12 citations), he introduced a GWO-based multi-stage algorithm for estimating the parameters of Permanent Magnet Direct Current (PMDC) motors, a breakthrough that enables precise controller tuning for wheeled mobile robots. He further advanced the field by combining learning from demonstration with the GWO algorithm for trajectory optimization (5 citations), and by adapting Ultra-Wideband positioning systems for improved indoor robot localization using Adaptive Monte Carlo methods (4 citations). Through these contributions, Pawlowski is helping to build more reliable and autonomous robotic systems capable of operating in complex, GNSS-denied environments.