Trajectory optimization method for spraying robot based on reinforcement learning

A spraying robot and trajectory optimization technology, applied in the direction of instruments, two-dimensional position/channel control, non-electric variable control, etc., can solve problems such as difficult to achieve dynamic real-time planning, affect the effect of manipulator control, and large amount of calculation. Achieve high success rate, high accuracy, and overcome poor real-time performance

Active Publication Date: 2019-03-26
NANJING QIANYUE ROBOT TECH CO LTD
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Problems solved by technology

When this type of method will model the environment or simulate the space, it needs to sample various postures of the manipulator, and judge whether the current action is reasonable through kinematic equations, and the amount of calculation is large; when obstacles and target positions in the environment occur When changing, it is necessar

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  • Trajectory optimization method for spraying robot based on reinforcement learning
  • Trajectory optimization method for spraying robot based on reinforcement learning
  • Trajectory optimization method for spraying robot based on reinforcement learning

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Embodiment

[0045] In this embodiment, the binocular vision system in the spraying robot trajectory optimization method based on reinforcement learning, such as figure 2 As shown, the left and right cameras can be placed in parallel, and the baseline must not be too long. The left and right cameras in the parallel optical axis binocular vision system are rotated clockwise and counterclockwise by a certain angle around the optical center to form convergent binocular vision. system; the advantage of this system is that it can obtain a larger field of view, and the advantage of a large field of view is that it can improve the accuracy of calculating parallax, thereby improving the accuracy of 3D reconstruction.

[0046] In this embodiment, the trajectory optimization method of the spraying robot based on reinforcement learning, such as figure 1 As shown, through image acquisition, image processing, three-dimensional reconstruction of graphics, graphics discretization, selection of initial p...

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Abstract

The invention relates to a trajectory optimization method for a spraying robot based on reinforcement learning. The trajectory optimization method determines the optimal spraying trajectory through image acquisition, image processing, graphic three-dimensional reconstruction, graphic discretization, initial point selection and optimal trajectory strategy selection, wherein the image processing includes camera calibration, image correction and stereo matching. The advantages of the invention lie in that the trajectory optimization method for the spraying robot based on reinforcement learning can plan a feasible path according to different states in different environments, is short in decision-making time and high in success rate and can meet the real-time requirements of online planning, thereby overcoming the shortcomings of poor real-time performance and great calculation amount of the traditional manipulator path planning method.

Description

technical field [0001] The invention belongs to the field of intelligent algorithm control, in particular to a trajectory optimization method for a spraying robot based on reinforcement learning. Background technique [0002] Most of the spraying robots use teaching spraying, and spraying according to the trajectory preset by the technical staff. This spraying method is effective in spraying according to the previous spraying experience, but the early programming work is relatively large, and the trajectory optimization is not obvious. Efficiency Low, the paint waste is relatively large. [0003] Trajectory optimization based on reinforcement learning uses reinforcement learning to model the environment through robots, and simulates the same or similar conditions as the environment inside the machine to optimize the spraying trajectory. Among the existing trajectory optimization algorithms, they are usually probabilistic road map method, fast search tree method and artifici...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0251G05D1/0276Y02T10/40
Inventor 宦键王馨馨陈伟王伟然智鹏飞刘俊杰刘浩
Owner NANJING QIANYUE ROBOT TECH CO LTD
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