Cluster robot mapping system and method

Abstract

The invention discloses a cluster robot mapping system and method, wherein the method comprises the steps that a sweeping robot builds a grid map through a laser radar, local sub-maps are transmittedto a control center in real time in a local area network communication module built based on a router, and finally on the basis of a known initial relative pose of a robot, the local sub-maps are converted and fused into a complete map through a map coordinate system. According to the cluster sweeping robot real-time mapping method based on the local area network, the problem that the mapping timeis long in a large-area indoor environment can be solved, meanwhile, the mapping quality can be improved, and the mapping effect can be observed in real time.

Description

technical field [0001] The invention relates to the field of mapping by cluster sweeping robots, in particular to a real-time mapping system for cluster robots based on a local area network. Background technique [0002] Robots are gradually replacing humans to complete basic tasks. The most common is the sweeping robot, which can help us clean up and is deeply loved by the people. However, as the cleaning environment changes, for example, the area becomes larger and the structure becomes more complex, the task completion efficiency of a single sweeping robot gradually decreases, and even the perception of the environment also produces certain errors with long-term walking, which affects work efficiency. Therefore, the work of cluster robots has become a research hotspot. The existing feature point matching and fusion methods require a large overlapping area, which cannot be fused in real time. Contents of the invention [0003] The technical problem to be solved by the ...

AI Technical Summary

Problems solved by technology

However, as the cleaning environment changes, for example, the area becomes larger and the structure becomes more complex, the task completion efficiency of a single sweeping robot gradually decreases, and even the perception of the environment also produces certain errors with long-term walking, which affects work efficiency.

Therefore, the work of cluster robots has become a research hotspot. The existing feature point matching and fusion methods require a large overlapping area, which cannot be fused in real time.

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