Innovative intelligent navigation system by applying fuzzy logic and cell assemblies in rescuing robot

In this study, an intelligent navigation system is developed by using fuzzy logic and Cell Assemblies (CAs) approaches, together with various sensors, in accordance with mimicking human behavior. Because disaster areas can be extremely dangerous and might cover large areas, it is important to make the robot's movement safer and more efficient. Therefore, two models with different functions are designed. The robot navigation model is applied to a narrow or dark place by using a laser range finder whereas the intelligent cognitive model uses intelligent direction change in an open area by integrating a vision camera. The intelligent cognitive model that is implemented by CAs with fatiguing Leaky Integrate and Fire (fLIF) neurons can not only absorb the ideals of working and long-term memories to explain the phenomenon of biology neurons, but can also imitate human cognitive processes. Additionally, it is not difficult to combine and expand this model to become multifunctional, which mimics human learning. The main contribution of this study is that the clever combination of sensors significantly reduces the frequency of use of vision and image processing. Furthermore, the combined system can avoid potential risks and efficiently shorten the motion time by importing the vision camera. This system has been tested in several simulated environment schemes and the experimental results have proven that it can produce correct action commands relative to different schemes and can also improve the motion path to effectively meet the requirements of disaster relief.