Mechanical arm autonomous obstacle avoidance method based on improved RRT algorithm

Abstract

The invention discloses a mechanical arm autonomous obstacle avoidance method based on an improved RRT algorithm. The mechanical arm autonomous obstacle avoidance method comprises the steps that S1, modeling is conducted on the pose of a six-axis mechanical arm through a D-H method; S2, a six-axis mechanical arm working environment based on gazebo is established, and obstacles in irregular shapes are randomly generated in the working environment; S3, motion planning of the mechanical arm is completed through the improved RRT algorithm; and S4, finally, smoothing processing is carried out on an obtained path by using a cubic Hermite curve. The mechanical arm autonomous obstacle avoidance method based on the improved RRT algorithm has the advantages that the directivity of sampling points can be improved, the smoothness of the path is increased, useless search of a space region is reduced, and the search time is saved.

Description

technical field [0001] The invention relates to the technical field of mechanical arm motion planning, and relates to a motion planning method in the obstacle avoidance link of a mechanical arm, in particular to an autonomous obstacle avoidance method for a mechanical arm based on an improved RRT algorithm. Background technique [0002] With the rapid development of my country's national economy, the demand for social logistics is also increasing. However, with the decline of China's working-age labor force, the logistics industry, a labor-intensive industry, has been greatly affected by the surge in labor costs. [0003] As a robot with handling function, the robotic arm has gradually attracted the attention of logistics companies. When the working environment of the manipulator changes, the fixed motion trajectory cannot help the manipulator adapt to the environmental change, thus burying a safety hazard for industrial production. At this time, the robot arm needs to plan...

AI Technical Summary

Problems solved by technology

The graph search method needs to construct a complete workspace. In high-dimensional space, the calculation amount of this method increases geometrically with the spatial dimension; the ant colony algorithm converges slowly in high-dimensional space, and most of them are local optimal; the artificial potential field method In high-dimensional space, the potential field is not easy to establish, and it is easy to fall into local optimum; while the rapid extended random tree method randomly samples the state space and performs collision detection on the sampling points, so as to realize the fast configuration space of the manipulator. Search, this method has fast planning speed and wide application, but there are problems such as node expansion lacks memory, the obtained path cannot be directly used by the robot, and the amount of calculation is large.

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