Mechanical arm pushing and grabbing system suitable for dense environment

Abstract

The invention discloses a mechanical arm pushing and grabbing system suitable for a dense environment. The system comprises a PC, a color depth camera, a mechanical arm and an intelligent clamping device; the software end comprises a CoppeliaSim simulation platform; the color depth camera is connected with the computer through a USB, and the mechanical arm is connected with the PC through a local area network; the color depth camera acquires a color RGB image and a depth image; the computer executes a mechanical arm control program; the mechanical arm executes actions; the intelligent clamper is used for grabbing or pushing the object block; and the CoppeliaSim simulation platform comprises a simulation module, a calibration module, an image preprocessing module, a feature extraction module, a decision network module, an action strategy module, a robot I / O module and a robot module. According to the method, simulation and reality are combined, robot damage caused by training is reduced, and the grabbing speed is increased; and the DQN of deep reinforcement learning is utilized, action semantics are dynamically planned in real time according to the current object environment, pushing and grabbing are combined, meanwhile, the most suitable grabbing direction is planned, and the success rate of grabbing the complex environment by the mechanical arm is greatly increased.

Description

technical field [0001] The invention relates to a mechanical arm pushing and grasping system suitable for dense environments Background technique [0002] With the development of the economy and the acceleration of the aging process of the population, the society's demand for practical grasping robots is increasing. However, when different industrial robots grab workpieces in a structured environment, service robots will face many situations, including how to efficiently grab the required targets in a dense environment. However, as far as most of the existing grasping systems are concerned, most of them are only suitable for scenes with large gaps between objects. When faced with dense object arrangements, there is not enough space around the objects for the mechanical grippers to grasp. Objects, planning to grasp becomes very difficult. M.R.Dogar and other researchers published A planning framework for nonprehensile manipulation under clutter and uncertainty in 2012, prop...

AI Technical Summary

Problems solved by technology

However, due to the joint torque as the output of the system, the sample complexity is extremely high, resulting in long training time and expensive system.

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