Shovel aligning system and shovel aligning method based on Internet of Vehicles data compensation strategy

Abstract

The invention discloses a shoveling system and shoveling method based on an Internet of Vehicles data compensation strategy. Belongs to the technical field of self-driving mine cars. Before shoveling, shoveling cannot be carried out due to the fact that backshoveling cannot be detected; and in the shovel aligning process, shovel aligning fails due to the fact that backacting can not be detected, and the traffic collision risk exists. A data sending module is electrically connected with a data receiving module to publish backhoe position data in real time, the data receiving module is electrically connected with a data fusion module to receive the position data published by a backhoe in real time, and the data fusion module is electrically connected with a backhoe control module. And the shoveling control module executes shoveling operation according to the obstacle data output by the data fusion module. The method is used for shovel alignment based on the car networking data compensation strategy.

Description

Technical field: [0001] The invention relates to a shovel shovel system and shovel shovel method based on a vehicle networking data compensation strategy. Background technique: [0002] In the unmanned operation process of smart mines, the trucks need to reverse the shovel operation according to the position of the backhoe. Before shoveling, if the backhoe cannot be detected, the shovel cannot be shoveled; during the shoveling process, if the backhoe is not detected, the shovel will fail, and there is a risk of collision. [0003] At present, there are three main schemes for autonomous driving obstacle detection; [0004] 1. Lidar technology. Lidar emits millions of laser beams in one second, and accurately calculates the shape and distance of surrounding objects through the time difference returned. Massive information perception capabilities allow autonomous vehicles to accurately and stably perceive the surrounding environment; [0005] 2. Computer vision technology. ...

AI Technical Summary

Problems solved by technology

[0008] Solution 1 Lidar technology is excellent in calculating object size and moving speed, but the object recognition ability is weak; in addition, Lidar is expensive and costly;

[0009] Scenario 2: The ranging ability of computer vision technology is far inferior to that of lidar, and it is greatly affected by the surrounding environment. There is a possibility of misjudgment in extreme weather or poor light conditions;

Images