Robot data acquisition layer structure

Abstract

The invention discloses a robot data acquisition layer structure. A support platform is mounted at the top end of a trunk frame of a robot, and Kinect and a network camera set are detachably and fixedly connected to the support platform in sequence from bottom to top. The bottom end face of the Kinect partly protrudes outwards to form two or more guiding pillars. The top end face of the support platform is partly sunken inwards to form guiding grooves matched with the guiding pillars. The network camera set comprises two or more network cameras. The top end face of an outer shell of the Kinectis partly sunken inwards to form two or more annular sliding grooves. The network cameras are horizontally arranged, and a rotating shaft is vertically and downwards formed in the middle of each network camera. The tail end of each rotating shaft is rotatably connected to the outer shell of the Kinect. A sliding rod is vertically and downwards formed at the movable end of one side of each networkcamera. The bottom end of each movable rod is arranged in the corresponding annular sliding groove in a slidable and embedded manner and can be fastened and located through a limiting part. The robotdata acquisition layer structure is compact in assembly; the acquisition angle can be adjusted fast and conveniently; and the flexibility is high.

Description

technical field [0001] The present invention relates to the field of robots, in particular to a robot data acquisition layer structure. Background technique [0002] With the development of industrial automation, machine intelligence has received more and more attention, among which intelligent robots are recognized as the mainstream development direction in the future. [0003] Once the robot wants to move, it will inevitably involve the understanding of the environment, path planning, autonomous positioning, obstacle avoidance and collision avoidance, etc., and the understanding of the environment and the autonomous positioning of the robot are the basis of other functions and the primary research direction . [0004] Existing researchers and application organizations use GPS to achieve robot autonomous positioning, and use manual input of known environmental maps and building structure diagrams to replace robots' autonomous learning and building maps, but they have the f...

AI Technical Summary

Problems solved by technology

[0004] Existing researchers and application organizations use GPS to achieve robot autonomous positioning, and use manual input of known environmental maps and building structure diagrams to replace robots' autonomous learning and construction of maps, but they have the following defects: First, GPS positioning is only applicable to In the outdoor environment, it is not suitable for indoor scenes; secondly, the civilian GPS positioning accuracy is not enough to meet the working accuracy requirements of the robot; secondly, the way of manually inputting maps is not suitable for scenes where the environment will change, such as: road blockage, Someone or something is occupied, the original road is cut off, etc. Then, the way of manually inputting the map limits the working range of the robot, and the robot can only work within the range determined by the map, and an accurate map is needed every time the working environment is changed Input; finally, the way of manually inputting the map makes the robot unable to work in the environment and scene without electronic file map

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