Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot’s head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.

In the theoretical controller design of the high-speed supercavitating vehicle (HSSV), there will always be the problem that the physical saturation limit has to be exceeded by the motion range of the actuator to satisfy the requirements of stable motion of the supercavitating vehicle. This paper proposes a solution which could satisfy the requirements of stable motion of the vehicle without saturation of the actuator. First of all, the rotation range of the actuator and the motion performance of the vehicle with robust controller are analyzed under the condition where saturation is neglected. Then, according to the analysis conclusion, the controller is improved by using linear active disturbance rejection control (LADRC) method, which provides the additional control component to reduce the rotation angle and rotation speed of the actuator. Finally, the simulation proves that the solution could realize the stable motion of vehicle without saturation of actuator.