# A Brain-Inspired Goal-Oriented Robot Navigation System
## 1 Introduction  
Animals have innate skills to navigate in unfamiliar environments [1]. It seems effortless for animals such as rats and primates but has been a fundamental long-term challenge for robots. Developing a brain-like navigation model based on the brain navigation mechanism of animals has attracted increasing attention. The hippocampus, one of the most important brain regions, which codes for direction, distance, and speed signals of movement, is thought to play a pivotal role in place learning, navigation, and exploration. Place cells in the CA1 field of the hippocampus have a conspicuous functional characteristic of location-specific activity. They have a high firing rate when animals are in a particular location and may form a map-like representation of the environment, called grid cells space. Animals can use grid cells space for efficient navigation [2]. The grid cells found by [PERSON] are also key cells for navigation [3; 4; 5]. Grid cells in the entorhinal cortex (EC) are arranged in strikingly periodic firing fields. A regularly tessellating triangular grid-like pattern was found to cover an entire environment, regardless of external cues. This suggests that grid cells have the ability to take in path integrals by internally generating a mapping of the spatial environment, which has been proved by an attractor network structure [6; 7]. Head-direction (HD) cells exist in the postsubiculum and the EC [8]. Like a compass, they provide the input of biological plasticity for the brain-like navigation system [9]. The discharge frequency of speed cells found in the medial entorhinal cortex (MEC) is regulated by running speed, which could dynamically represent the animal's location [10]. Integrating HD cells and speed cells, animals have knowledge of their direction and speed of motion and can track their idiothetic motion [11]. Navigation also involves memory and retrieval of environmental information, which is considered a cognitive mechanism for storing and processing information. A navigation system with spatial cognition and mapping can better achieve rapid and stable navigation. Anatomically, the neural responses of working memory are found in large areas of the parietal and frontal cortex. Accumulating evidence suggests that the posterior parietal cortex (PPC) and the dorsolateral prefrontal cortex (DLPFC) in the cerebral cortex are closely involved in working memory [12; 13; 14], temporary storage, and manipulation of data related to cognitive control [15]. Adaptive resonance theory (ART) is a cognitive and neural theory of how the brain autonomously learns to categorize and recognize events in a changing world. In this paper, the ART mechanism was used to deal with the retrieval and storage of environmental information. The rewards are a positive incentive for the desired action. The rewards will increase the selected probability of the previous action and guide the animal to easily reach the final goal. Anatomically, the orbitofrontal cortex (OFC) is involved in the process of reward signals [16]. In this paper, the rewards are represented by value functions of Q-learning to aid navigation. Numerous computational models have been proposed based on place cells and grid cells. Each place cell has its own firing field and, taken together, the firing fields of place cells tend to cover the entire surface of the environment. The firing fields of place cells have mainly been modeled as Gaussian functions [17; 18; 19]. A large majority of grid cells have been modeled as three classes: oscillatory interference [20; 21; 22], continuous attractor network [6; 7; 23], and self-organizing [24; 25; 26]. These models concentrate only on the formation of place cells and grid cells, which does not really apply to navigation and is not instructive for autonomous robotics navigation. Many robot navigation systems use a brain-like computational mechanism to complete navigation tasks. [PERSON] developed a bionic navigation algorithm called RatSLAM [27; 28].