J. Minners
Papers
1
Total Citations
2
H-Index
1
About
J. Minners is a researcher whose work bridges robotics, neural networks, and autonomous systems. Their most notable contribution lies in the development of self-organizing neural networks for real-time spatial mapping in robotic systems. In their 2002 paper, Minners introduced a methodology that enables mobile robots to autonomously learn and construct maps of their environment using only range sensors, with maps referenced to the robot’s inertial frame. This approach allows for real-time correlation between robot position and sensor data, advancing the field of autonomous navigation. While the paper has garnered 2 citations, its conceptual foundation has influenced subsequent work in robotic mapping and neural network applications. Minners’ research is particularly relevant for students and researchers interested in embodied cognition, sensor-based learning, and the intersection of artificial intelligence with robotics. Their work underscores the potential for self-organizing systems to enable adaptive, real-world spatial understanding in autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1