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Fast expansion random tree and potential field method based visual servo path planning method

A technology of visual servoing and path planning, which is applied in the field of robotics and can solve problems such as collisions

Pending Publication Date: 2019-07-05
CENT SOUTH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are still some problems. If you need to maintain continuous visibility of useful image information, you need to avoid collisions between the robot and space obstacles or targets, etc.

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  • Fast expansion random tree and potential field method based visual servo path planning method
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  • Fast expansion random tree and potential field method based visual servo path planning method

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Embodiment Construction

[0060] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0061] The present invention is based on the following principles: the basic idea of ​​the potential field method is to set a repulsive potential field at the obstacle, and a gravitational potential field at the target position, and determine the movement direction of the mechanical arm by the resultant force of the two. The rapid expansion random tree method is a random search algorithm based on the data structure-tree, which can quickly search for an effective collision-free path. Combine the two, use the idea of ​​the potential field method to construct the cost function, and optimize and quickly expand the random tree method.

[0062] The application scenario of this embodiment is as follows figure 2 As shown, it includes a six-degree-of-freedom robot body fixed on the base, a manipulator as an end effector tha...

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Abstract

The invention discloses a fast expansion random tree and potential field method based visual servo path planning method. The method includes giving mapping relation between task space coordinates andimage plane pixel coordinates of target feature points of a robot under any pose; and randomly producing new poses in task space, no obstacles between the new poses and the closest original pose beingguaranteed, and calculating the image plane pixel coordinates of the target feature points under the corresponding poses so as to maintain the image plane pixel coordinates in camera visual field scope. The method searches feasible camera paths in the task space to obtain corresponding image plane feature trajectories, and utilizes an image based visual servo (IBVS) to perform tracking control sothat the robot can realize the obstacle avoidance from an initial pose to an expected pose, and visual field constrained motion can be met. Through the introduction of a path planning technology in the visual servo of the robot, the problems of obstacle avoidance and feature visibility in the visual servo of the robot can be solved.

Description

technical field [0001] The invention relates to the technical field of robots, in particular to a visual servo path planning method based on a rapidly expanding random tree and a potential field method. Background technique [0002] In recent decades, robot visual servo control technology has developed rapidly. It helps robots perceive changes in the external environment through visual information feedback, guides robots to move to desired positions, and greatly improves the level of robot intelligence. However, there are still some problems. If you need to maintain continuous visibility of useful image information, you need to avoid collisions between the robot and space obstacles or targets. [0003] Path planning is an important part of robot motion planning, which can well realize spatial obstacle avoidance and limit the robot's activity area. Therefore, path planning is introduced into visual servoing to provide it with feature trajectories that can satisfy multi-space...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0251G05D1/0221G05D1/0285
Inventor 徐德刚洪松涛苏志芳阳春华
Owner CENT SOUTH UNIV
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