# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 4 Biological Intelligence and Agricultural Aviation  
### Brain-Like Navigation  
Brain-like navigation has received extensive attention in recent years and has become one of the research hotspots in the field of artificial intelligence. In 1984, [PERSON] proposed that animals understand the environment through \"cognitive maps\" [69], and scholars have begun to explore animal brain navigation. In 1971, [PERSON] found place cells in the hippocampus of rats, then head direction cells, grid cells, border cells, etc., were discovered one after another. The mammalian brain has its own intelligent navigation system, which can form a cognitive map for positioning and navigation of the surrounding environment. This is related to the cells in the brain. The navigational cells mainly include head direction cells, grid cells, place cells, and border cells [70]. Head direction cells are responsible for recognizing the animal's position in the environment, and act as a compass in animal navigation. Grid cells and place cells cooperate to draw a cognitive map. Cells of the brain-like navigation system work together to continuously update the error-correcting cognitive map for precision navigation. The schematic diagram of the mammalian brain navigation system is shown in Figure 8.  
Since humans have not yet fully understood the mechanism of the mammalian brain, the current development of brain-like navigation technology is at the stage of theoretical research. [PERSON] [70] analyzed the key technology of the neural dynamics model combined with the neural network and attractor dynamics for navigation. This technology does not require modeling, does not require high sensor accuracy, is suitable for a variety of complex environments, and is robust. It has a strong development prospect. [PERSON] pointed out that how to simulate the brain navigation mechanism from a deeper level, and how to improve the efficiency, accuracy, and real-time capabilities of the brain-like navigation model are the focuses for future research. [PERSON] [71] proposed a grid cell construction model based on differential Hebbian learning. This model builds the input correlation between grid cells and the Mexico model by learning the change value of the positional cell discharge rate. This model provides a reference for the construction of UAVs' brain-like navigation system. [PERSON] [72] used a one-dimensional ring attractor to model the head-facing cells, and used the sinusoidal interference model to obtain the grid-like activity pattern, and finally obtained the optimal movement direction according to all the activity sets, which solved the problem of dynamic adjustment of the network. It is effective and reliable, with superior navigation performance.  
Although brain-like navigation technology is the current hotspot of artificial intelligence, in-depth research on the brain and neurons is still needed. Brain-like navigation can realize unsupervised and autonomous operation of plant protection UAVs, abandon the unreliability of GPS (Global Positioning System) navigation due to signal fluctuations, and can autonomously respond to complex unknown environments such as light, rain, snow, and low visibility, so as to achieve precision agricultural aviation. At present, the research of brain-like navigation is not perfect, and the problems of large environment storage and calculation, and poor stability caused by imperfect algorithms still need to be solved.