Robot Obstacle Avoidance Based on Improved Artificial Potential Field Method

2021 ◽  
Author(s):  
Ye Hanli ◽  
Liu Qiliang ◽  
Zhang Weizhen
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Potential Field Method ◽  
Artificial Potential Field Method

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianying Xu ◽  
Haibo Zhou ◽  
Shuaixia Tan ◽  
Zhiqiang Li ◽  
Xia Ju ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Planning Process ◽  
Field Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Content Type ◽  
Potential Field Method ◽  
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.

scholarly journals Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

2020 ◽  
Vol 10 (3) ◽  
pp. 935 ◽  
Author(s):  
Haibo Zhou ◽  
Shun Zhou ◽  
Jia Yu ◽  
Zhongdang Zhang ◽  
Zhenzhong Liu
Keyword(s):  
Genetic Algorithm ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Trajectory Optimization ◽  
Field Method ◽  
Artificial Potential Field ◽  
Adaptive Genetic Algorithm ◽  
Potential Field Method ◽  
Kinematics Modeling ◽  
Artificial Potential Field Method

In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

scholarly journals Improved Manipulator Obstacle Avoidance Path Planning Based on Potential Field Method

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ming Zhao ◽  
Xiaoqing Lv
Keyword(s):  
Path Planning ◽  
Success Rate ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Working Environment ◽  
Artificial Potential Field ◽  
End Point ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Aiming at the existing artificial potential field method, it still has the defects of easy to fall into local extremum, low success rate and unsatisfactory path when solving the problem of obstacle avoidance path planning of manipulator. An improved method for avoiding obstacle path of manipulator is proposed. First, the manipulator is subjected to invisible obstacle processing to reduce the possibility of its own collision. Second, establish dynamic virtual target points to enhance the predictive ability of the manipulator to the road ahead. Then, the artificial potential field method is used to guide the manipulator movement. When the manipulator is in a local extreme or oscillating, the extreme point jump-out function is used in real time to make the end point of the manipulator produce small displacements and change the action direction to effectively jump out of the dilemma. Finally, the manipulator is controlled to avoid all obstacles and move smoothly to form a spatial optimization path from the start point to the end point. The simulation experiment shows that the proposed method is more suitable for complex working environment and effectively improves the success rate of manipulator path planning, which provides a reference for further developing the application of manipulator in complex environment.

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

2021 ◽  
Vol 16 ◽  
Author(s):  
Hongxin Zhang ◽  
Jiaming Li ◽  
Rongzijun Shu ◽  
Hongyu Wang ◽  
Guangsen Li
Keyword(s):  
Obstacle Avoidance ◽  
Binocular Vision ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Robot Arm ◽  
Potential Field Method ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Artificial Potential Field Method

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.

scholarly journals An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141879956 ◽  
Author(s):  
Wenrui Wang ◽  
Mingchao Zhu ◽  
Xiaoming Wang ◽  
Shuai He ◽  
Junpei He ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Potential Field ◽  
Lyapunov Stability Theory ◽  
Field Method ◽  
Artificial Potential Field ◽  
Redundant Manipulators ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

In this article, we present an improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Specifically, we not only focused on the position for the manipulator end-effectors but also considered their posture in the course of trajectory planning and obstacle avoidance. We introduced boundaries for Cartesian space components to optimize the attractive field function. Moreover, the manipulator achieved a reasonable speed to move to the target pose, regardless of the difference between the initial pose and target pose. We proved the stability using Lyapunov stability theory by introducing velocity feedforward, when the manipulator moved along a continuous trajectory. Considering the shape of the manipulator joints and obstacles, we set up the collision detection model by projecting the obstacles to link coordinates. In this case, establishing the repulsive field between the nearest points on every joint and obstacles with the closest distance was sufficient for achieving obstacle avoidance for redundant manipulators. The simulation results based on a nine-degree-of-freedom hyper-redundant manipulator, which was designed and made in our laboratory, fully substantiated the efficacy and superiority of the proposed method.

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