Joel Enrico Lehner

FHNW University of Applied Sciences and Arts

Papers

Total Citations

H-Index

About

Joel Enrico Lehner is a researcher whose work bridges the fields of robotics, artificial intelligence, and evolutionary computation. His primary research focuses on the optimization of multi-robot systems, particularly through the application of genetic algorithms to improve autonomous decision-making and physical interaction strategies. In his most-cited paper, "Optimization of Multi-Robot Sumo Fight Simulation by a Genetic Algorithm to Identify Dominant Robot Capabilities" (2019), Lehner developed a computational model where sumo-style robots compete by physically maneuvering opponents out of an arena. By employing a genetic algorithm, he systematically identified which robot capabilities—such as speed, pushing force, or sensor accuracy—most strongly influence success in competitive, dynamic environments. This work, with 3 citations, provides foundational insights into how evolutionary techniques can optimize multi-agent coordination and physical interaction, with implications for swarm robotics and autonomous systems. Lehner’s contributions are notable for their practical approach to simulating complex, real-world robotic competitions, offering a scalable framework for designing more capable and adaptive robots. His research continues to inspire students and researchers interested in the intersection of optimization, robotics, and artificial life.