We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path planning task into different levels of sub-tasks in complex environments and solves each sub-task in a smaller state subspace to decrease the state space dimensions. The HCNM model imitates animal asymptotic properties through the study of thermodynamic processes and designs a cognitive learning algorithm to achieve online optimum search strategies. We prove that the learning algorithm designed ensures that the cognitive model can converge to the optimal behavior path with probability one. Robot navigation is studied on the basis of the cognitive process. The experimental results show that the HCNM model has strong adaptability in unknown and environment, and the navigation path is clearer and the convergence time is better. Among them, the convergence time of HCNM model is 25 s, which is 86.5% lower than that of HRLM model. The HCNM model studied in this paper adopts a hierarchical structure, which reduces the learning difficulty and accelerates the learning speed in the unknown environment.

The brain is remarkably responsive to its interactions with the environment, and its morphology is altered by experience in measurable ways. Histological examination of the brains of animals exposed to either a complex (‘enriched’) environment or learning paradigm, compared with appropriate controls, has illuminated the nature of experience-induced morphological plasticity in the brain. For example, this research reveals that changes in synapse number and morphology are associated with learning and are stable, in that they persist well beyond the period of exposure to the learning experience. In addition, other components of the nervous system also respond to experience: oligodendrocytes and axonal myelination might also be permanently altered, whereas changes in astrocytes and cerebrovasculature are more transient and appear to be activity- rather than learning-driven. Thus, experience induces multiple forms of plasticity in the brain that are apparently regulated, at least in part, by independent mechanisms.