Michael Schanz
Papers
13
Total Citations
115
H-Index
6
About
Michael Schanz’s research lies at the intersection of autonomous mobile robotics, neural network control, and self-organizing multi-agent systems. His most influential work, “Kinematic and Dynamic Adaptive Control of a Nonholonomic Mobile Robot using a RNN” (23 citations), introduced a two-level adaptive neurocontrol system that uses recurrent neural networks to enhance kinematic controller robustness and generate precise linear and angular velocities for trajectory tracking. Schanz also pioneered the application of pattern formation principles—drawn from biological, chemical, and physical systems—to solve distributed robot-target assignment problems, as demonstrated in his 1998 paper on self-organized behavior (21 citations) and subsequent experimental studies on error-resistant control (12 citations). His work on velocity control for omnidirectional RoboCup players (17 citations) further showcases his ability to integrate recurrent neural networks into real-time robotic platforms. A key contribution is his dynamic task assignment framework using coupled selection equations, which he applied to multi-agent teams and RoboCup scenarios. With over 100 total citations, Schanz’s research has advanced adaptive control and decentralized coordination, offering foundational insights for students and researchers in autonomous systems and swarm robotics.
Research Focus
Key Achievements
Top Papers
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- 6CoPS-Team Description6 citations · 2001
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- 8NEURAL FIELDS FOR BEHAVIOR-BASED CONTROL OF MOBILE ROBOTS4 citations · 2006
- 9Dynamic Task Assignment in a Team of Agents4 citations · 2006
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