Multi-clamp mechanical arm disordered grabbing method and system

A robotic arm, multi-fixture technology, applied in the direction of manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as inability to batch batch objects, and achieve the effect of efficient processing

Active Publication Date: 2021-09-03
常州唯实智能物联创新中心有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The multi-fixture robot on the existing automatic assembly line can only batch process a large number of objects that have been pre-processed and neat, and cannot batch process multiple objects with different motion states. Objects have different moving ...

Method used

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  • Multi-clamp mechanical arm disordered grabbing method and system
  • Multi-clamp mechanical arm disordered grabbing method and system
  • Multi-clamp mechanical arm disordered grabbing method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] Such as figure 1 As shown, Embodiment 1 provides a disorderly grasping method of a multi-gripper robotic arm. By predicting and simulating the motion state, and selecting the optimal grasping order through the Monte Carlo tree search analysis method, a better solution can be obtained in a short time, so that the task of grasping multiple objects can be handled efficiently.

[0053] Specifically, the disorderly grasping method of the multi-gripper robotic arm includes:

[0054] S110: Obtain the current motion state of the object group to be grasped.

[0055] Specifically, step S110 includes the following steps:

[0056] S111: Collect a single frame of image to classify and recognize objects in the scene, and determine the classification of objects in the scene.

[0057] In this embodiment, the image capture is performed by the camera device attached to the robotic arm, and the objects are classified according to the pre-stored classification standards for grasping re...

Embodiment 2

[0088] see image 3 , the present embodiment provides a multi-gripper robotic arm out-of-order grasping system, the system includes: a motion state acquisition module, a motion trajectory prediction module, and a Monte Carlo tree search and analysis module.

[0089] The motion state acquisition module is adapted to obtain the current motion state of the object group to be grasped; specifically, the motion state acquisition module is used to perform the following steps:

[0090] S111: Collect a single frame of image to classify and recognize objects in the scene, and determine the classification of objects in the scene.

[0091] In this embodiment, the image capture is performed by the camera device attached to the robotic arm, and the objects are classified according to the pre-stored classification standards for grasping requirements.

[0092] S112: Determine the number of objects to be grasped according to the object classification result.

[0093] S113: Continuously colle...

Embodiment 3

[0121] This embodiment provides a computer-readable storage medium, and at least one instruction is stored in the computer-readable storage medium. When the above-mentioned instructions are executed by a processor, the multi-gripper robotic arm disorder provided by Embodiment 1 is realized. Grab method.

[0122] The disorderly grasping method of the manipulator with multiple fixtures predicts and simulates the motion state, and selects the optimal grasping sequence through the Monte Carlo tree search analysis method, so that a better solution can be obtained in a short time, so that Efficiently handle the task of grasping multiple objects.

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Abstract

The invention relates to a multi-clamp mechanical arm disordered grabbing method and system.The multi-clamp mechanical arm disordered grabbing method comprises the steps that the current motion state of a to-be-grabbed object set is obtained; predicting of a motion track of each object in the to-be-grabbed object set is conducted; and an optimal grabbing sequence is selected through a Monte Carlo tree search analysis method according to the motion track of each object. The motion state is predicted and simulated, the optimal grabbing sequence is selected through the Monte Carlo tree search analysis method, an optimal solution can be obtained in a short time, and therefore the task of grabbing multiple objects can be efficiently processed.

Description

technical field [0001] The present invention relates to the field of robot grasping path planning, in particular to a random grasping method and system of a multi-clamp robotic arm. Background technique [0002] With the advent of the wave of artificial intelligence, intelligent robots have gradually replaced traditional automation equipment in various industries. For robots, grasping is an essential skill for robots to complete their work. In the past, the robotic arm often only had a single gripper, and could only grasp a single object in one grasp. However, in actual production application scenarios, it is often necessary to grasp multiple objects to speed up production and processing efficiency. . [0003] The multi-fixture robot on the existing automatic assembly line can only batch process a large number of objects that have been pre-processed and neat, and cannot batch process multiple objects with different motion states. Objects have different moving trajectories...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1602B25J9/1697B25J9/1679
Inventor 秦广军林浩田肖利民韩萌杨钰杰王良孙锦涛
Owner 常州唯实智能物联创新中心有限公司
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