Lazaros Moysis
Papers
10
Total Citations
422
H-Index
7
About
Lazaros Moysis is a leading researcher at the intersection of nonlinear dynamics, chaos theory, and autonomous robotics, whose work has redefined how mobile robots and UAVs explore and cover unknown environments. His most impactful contribution is the development of chaotic path planning generators—algorithms that leverage the inherent unpredictability of chaotic systems to produce highly efficient, pseudo-random motion commands for area coverage. By integrating techniques like the logistic map, modulo tactics, and memory-based methods, Moysis has solved the critical challenge of ensuring uniform, complete exploration while maintaining unpredictability, a feat unattainable with traditional deterministic or purely random approaches. His widely cited 2024 review on machine and deep learning for robotic vision (239 citations) further cements his influence, bridging chaos-based navigation with modern perception systems. With over 180 total citations across his top works, Moysis has demonstrated both theoretical depth—through dynamical analysis and synchronization of novel hyperjerk systems—and practical impact, including experimental implementations on microcontroller-based robots. His pioneering fusion of chaos theory with autonomous navigation continues to inspire new generations of roboticists and control engineers.
Research Focus
Key Achievements
Top Papers
- 1
- 2A chaotic path planning generator based on logistic map and modulo tactics64 citations · 2019
- 3
- 4A chaotic path planning generator enhanced by a memory technique27 citations · 2021
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- 7
- 8Chaotic Motion Control of a Mobile Robot Using a Memory Technique4 citations · 2020
- 9Experimental Coverage Performance of a Chaotic Autonomous Mobile Robot4 citations · 2022
- 10