Robot Navigation Strategy in Complex Environment Based on Episode Cognition

The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acquire episodic cognitive memory.Through episodic cognition and memory,organisms can achieve autonomous navigation in complex environments.This paper mainly studies the strategy of robot episode navigation in complex environments.After exploring the environment,the robot obtains subjective environmental cognition and forms a cognition map.The grid cells information contained in the cognitive map can obtain the direction and distance of the target through vector calculation,which can get a shortcut through the inexperienced area.The synaptic connection of place cells in the cognitive map can be used as the topological relationship between episode nodes.When the target-oriented vector navigation encounters obstacles,the obstacles can be realized by setting closer sub-targets.Based on the known obstacle information obtained from boundary cells in the cognitive map,topological paths can be divided into multi-segment vector navigation to avoid encountering obstacles.This paper combines vector and topological navigation to achieve goal-oriented and robust navigation capability in a complex environment.

Optimized shearing strategy for heavy plate based on contour recognition

The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed.Firstly,multi-array binocular vision linear cameras were used to complete the image acquisition.Secondly,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour data.Finally,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was calculated.The practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel plates.The maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 mm.The correlation coefficient of the length prediction model based on the back propagation neural network is very high.The reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.

Relay navigation strategy study on intelligent drive on urban roads

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.