Research on Dynamic Obstacle Avoidance Method of Manipulator Based
on Binocular Vision
Authors: Zhang Hongxin , Li Jiaming , Shu Rongzijun , Wang Hongyu , Li Guangsen 
Published: December 2021

Background:

With the development of robotics, more and more robots are being used in
industrial production. However, as the production environment surrounding the robots becomes increasingly
complex, there is a need for more intelligent industrial robots. For improving the intelligence
of industrial robots, the most important thing is to ensure that they can carry out safe industrial
production activities in a complex production environment. Therefore, studying the autonomous
dynamic obstacle avoidance path planning of industrial robots in complex environments is of great
significance for improving the intelligence of industrial robots and the application of human-machine
collaboration.

Objective:

The main purpose of this paper is to improve the traditional artificial potential field method.
It aims to improve the disadvantages of the traditional artificial potential field method, such as
falling into the local minimum and failing to reach the target. Secondly, the background difference
method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental
map containing the static and dynamic obstacles is obtained. After obtaining the information
on the position of static and dynamic obstacles, the robot arm can make good use of the improved
artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle
avoidance of the robot arm in a complex environment.

Methods:

This paper proposes an improved artificial potential field method. First of all, in order to
solve the problem of not being able to achieve the goal, the method of modifying the repulsion field
function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function,
the relative distance between the robot and the target is introduced. Due to this, the target point is
always the minimum point of the potential field. It is necessary to ensure that the robot can reach the
target position smoothly. For the local minimum problem, by adding a gravitational increase factor
β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of
the resultant force. Finally, for the traditional artificial potential field method that does not consider
the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic
obstacle is brought into the potential field function to improve the traditional artificial potential field
method.

Results:

The robot easily falls into the local optimum during path planning, and the improved artificial
potential field method overcomes this shortcoming. Therefore, the manipulator can perform
autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles,
finally reaching the target point safely.

Conclusions:

The industrial production field faces an increasing demand for intelligent industrial
robots. This paper improves on the traditional artificial potential field method based on binocular
vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic
obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology
to meet the needs of future industrial development and contribute to the development of future
industrial robot technology.