Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

Background: With the development of robotics, more and more robots are used in manufacturing. However, in actual work, safety accidents happen to robots from time to time. How to ensure the safe operation of robots in a limited and complex working environment is the key to improve robot technology. Therefore, it is of great significance to study the dynamic obstacle avoidance of robots in complex environment for improving the intelligence and safety of robots, and the application of human-robot collaboration. Objective: The primary purpose of this paper is to improve the traditional artificial potential field method, including he disadvantages that the improved target is inaccessible and easily plunged into local optimal solution of the drawback of the improved method, second. Secondly, the background difference method based on binocular vision and Kalman filtering algorithm, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the position information 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 complex environment. Methodology: The background difference method and the Kalman filtering algorithm based on binocular vision were introduced to track the dynamic obstacles, and the improved artificial potential field method for path planning was applied to the dynamic obstacle avoidance path planning of the manipulator. Finally, the simulation and experimental results show that under the complex environment with dynamic obstacles exist, robot arm can realize independent dynamic obstacle avoidance. Results: By using background difference method and Kalman filtering algorithm to track the target in real time, the result showed that the target could be detected and tracked well. By improving the defect that the traditional artificial potential field method is easy to fall into local optimum, the improved algorithm can well realize the dynamic obstacle avoidance of the manipulator. Conclusions: For the development requirements of the industrial robots in the future, this paper based on binocular vision, which can make the manipulator realize more intelligent industrial production activities in complex working environment, meet the needs of future industrial development, and make this technology play an important role in production activities.

Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Purpose This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process. Design/methodology/approach In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point. Findings Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability. Originality/value An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

Application of 6-Dof Robot Motion Planning in Fabrication

In practical robotic construction work, such as laying bricks and painting walls, obstructing objects are encountered and motion planning needs to be done to prevent collisions. This paper first introduces the background and results of existing work on motion planning and describes two of the most mainstream methods, the potential field method, and the sampling-based method. How to use the probabilistic route approach for motion planning on a 6-axis robot is presented. An example of a real bricklaying job is presented to show how to obtain point clouds and increase the speed of computation by customizing collision and ignore calculations. Several methods of smoothing paths are presented and the paths are re-detected to ensure the validity of the paths. Finally, the flow of the whole work is presented and some possible directions for future work are suggested. The significance of this paper is to confirm that a relatively fast motion planning can be achieved by an improved algorithmic process in grasshopper.