Grasping pose estimation method for multi-category out-of-sequence workpiece robot based on deep learning

Abstract

The invention discloses a method for estimating the grasping pose of a multi-category out-of-order workpiece robot based on deep learning. Three independent point cloud classification networks, position generation networks and attitude generation networks are used; the point cloud information is input to the point cloud classification network, and the input point cloud information is classified by the point cloud classification network to obtain the category of the point cloud information; The category of cloud information and point cloud information are combined to synthesize point cloud information. The point cloud information is input into the position generation network and the attitude generation network respectively. The position generation network and the attitude generation network process the point cloud information and predict the position. information and attitude information, and synthesize the robot's pose. The invention can realize the grasping pose estimation of multi-category disordered workpieces, and is a brand-new end-to-end implementation method based on deep learning, which can quickly realize the grasping of specific workpieces only by providing fewer sets of training data. programming to meet the needs of industrial production.

Description

technical field [0001] The invention relates to a method for estimating the pose of a robot grasping belonging to artificial intelligence, in particular to a method for estimating the pose of a robot grasping a multi-category out-of-sequence workpiece based on deep learning. Background technique [0002] As one of the world's top five industrial robot consumers, China's installation volume increased to 36.0% of the world in 2018. A total of 138,000 industrial robots were installed, a year-on-year increase of 59%. The consumption volume has exceeded the sum of Europe and the United States. Intelligent manufacturing is the main direction of Made in China 2025, and there is a huge demand for intelligent industrial robots. The application of robots for handling and loading and unloading accounts for more than two-thirds, and the added value brought by intelligent upgrading is obvious. [0003] With the development of artificial intelligence, some scholars have begun to study th...

AI Technical Summary

Problems solved by technology

However, traditional reinforcement learning methods have great limitations when solving high-dimensional state and action space problems. Under the condition of limited samples and computing units, the ability to express complex functions is limited, and the performance in practical applications is often not ideal.

At the same time, the traditional deep reinforcement learning algorithm needs to provide a large amount of data for training. During the training process, the robot needs to continue to grasp and try and make mistakes, so that it is possible to obtain a stable grasping ability.

This training method has a long cycle and low efficiency, and there are safety hazards in the actual training process, which often cannot meet the needs of industrial production applications.

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