Robot obstacle avoidance behavior learning and target searching method based on deep belief network

Abstract

The invention discloses a robot obstacle avoidance behavior learning and target searching method based on a deep belief network. The robot obstacle avoidance behavior learning and target searching method based on a deep belief network includes the steps: controlling a robot to realize obstacle avoidance in the environment, acquiring the color, the deep image data, and the linear velocity and the angular velocity corresponding to a mobile matrix of the robot at the same time, and based on the data, constructing a network model of implementing automatic obstacle avoidance behavior learning of the robot; during the automatic target searching process of the robot, randomly searching the target in the environment through the automatic obstacle avoidance function, and once searching the target,directly approaching the target, wherein if the obstacle appears during the approaching process, the robot can avoid from the obstacle and perform path planning again, and if the target is lost duringthe approaching process, the robot randomly searches again; and continuously repeating the above process until the robot arrives at the target position. The robot obstacle avoidance behavior learningand target searching method based on a deep belief network only uses a single RGB-D camera to realize path planning and target searching with the automatic obstacle avoidance function, and has higherfeasibility and practicality in the cost aspect and the application aspect.

Description

technical field [0001] The invention relates to the technical fields of machine learning and pattern recognition, in particular to a method for automatic obstacle avoidance behavior learning and target search of a robot based on a Deep Belief Network (DBN) in an unstructured environment. Background technique [0002] Automatic object search is a necessary skill for all kinds of robots in the current human environment, such as social robots, service robots, search and rescue robots, etc. to function well in unstructured environments. This skill requires the robot to have many other basic capabilities, including object recognition, automatic obstacle avoidance, path planning and navigation. Among them, it is the key to realize the automatic obstacle avoidance learning ability of the robot and the path planning ability from the starting point to the destination in an unknown environment. Although the above functional modules are very active in the research fields of computer v...

AI Technical Summary

Problems solved by technology

Although the above functional modules are very active in the research fields of computer vision and robotics, there are relatively few studies on the complete realization of target recognition, search and approaching on robotic systems through the learning of automatic obstacle avoidance, and only use a single RGB- There are relatively few studies on the automatic obstacle avoidance ability learning of robots with 3D cameras.

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