A Bio-Inspired Goal-Directed Cognitive Map Model for Robot Navigation and Exploration

The concept of a cognitive map (CM), or spatial map, was originally proposed to explain how mammals learn and navigate their environments. Over time, extensive research in neuroscience and psychology has established the CM as a widely accepted model. In this work, we introduce a new goal-directed cognitive map (GDCM) model that takes a nontraditional approach to spatial mapping for robot navigation and path planning. Unlike conventional models, GDCM does not require complete environmental exploration to construct a graph for navigation purposes. Inspired by biological navigation strategies, such as the use of landmarks, Euclidean distance, random motion, and reward-driven behavior. The GDCM can navigate complex, static environments efficiently without needing to explore the entire workspace. The model utilizes known cell types (head direction, speed, border, grid, and place cells) that constitute the CM, arranged in a unique configuration. Each cell model is designed to emulate its biological counterpart in a simple, computationally efficient way. Through simulation-based comparisons, this innovative CM graph-building approach demonstrates more efficient navigation than traditional models that require full exploration. Furthermore, GDCM consistently outperforms several established path planning and navigation algorithms by finding better paths.