Abstract: The present disclosure provides a method for estimating a body size and weight of a pig based on deep learning, and relates to the technical field of deep learning. The present disclosure predicts the weight of the pig by using a convolutional neural network. Relevant features are learned by the convolutional neural network, and feature engineering extraction is not needed to be established, so that extracted features are more comprehensive, and the convolutional neural network is superior to a linear model in processing of noisy data and nonlinear problems of data. Images of the pig are shot by an ordinary two-dimensional (2d) color camera.

Abstract: Provided are a method for rapidly determining a warehousing map, a device, a storage medium and a robot. The method includes: generating a warehouse map of a warehouse; acquiring a motion position and pose of the robot, and acquiring information of a mark on a rack collected by an image collection sensor on the robot; determining a mark position of the mark; determining a rack position of the rack and a depositary place position of each depositary place on the rack; and determining a warehousing map of the warehouse. The method can rapidly create the warehousing map in the warehouse, and position accuracy of the depositary place in the warehousing map is high.

Abstract: The present disclosure provides a distributed congestion control method and a robot. The method includes: disposing a first area and a second area centered on a target point; detecting, when a first robot moves towards the target point, whether there are other robots in the second area or not, and in a case where there are other robots in the second area, coordinating the first robot and the other robots to determine a second robot allowed to simultaneously enter the first area; and controlling the second robot to move towards the target point in a case where the second robot has entered the first area.

Abstract: The present disclosure provides a distributed congestion control method and a robot. The method includes: disposing a first area and a second area centered on a target point; detecting, when a first robot moves towards the target point, whether there are other robots in the second area or not, and in a case where there are other robots in the second area, coordinating the first robot and the other robots to determine a second robot allowed to simultaneously enter the first area; and controlling the second robot to move towards the target point in a case where the second robot has entered the first area.

Abstract: Provided are a method for rapidly determining a warehousing map, a device, a storage medium and a robot. The method includes: generating a warehouse map of a warehouse; acquiring a motion position and pose of the robot, and acquiring information of a mark on a rack collected by an image collection sensor on the robot; determining a mark position of the mark; determining a rack position of the rack and a depositary place position of each depositary place on the rack; and determining a warehousing map of the warehouse. The method can rapidly create the warehousing map in the warehouse, and position accuracy of the depositary place in the warehousing map is high.