Research on the Obstacle Avoidance Strategy for the Wave Glider in the Maritime Environment Containing Dynamic Obstacles Based on the Improved Artificial Potential Field Method

2020 ◽  
Author(s):  
Daoyong Wang ◽  
Xinliang Tian ◽  
Xiantao Zhang ◽  
Xiaoxian Guo ◽  
Peng Wang
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Avoidance Task ◽  
Marine Monitoring ◽  
Dynamic Obstacle Avoidance ◽  
Wave Glider ◽  
Avoidance Strategy ◽  
Dynamic Obstacle

Abstract As a wave-propelled and persistent unmanned surface vehicle, wave glider has been widely applied in marine monitoring. Due to its long voyage, the wave glider inevitably encounters various obstacles at sea, which may cause collision accidents. However, considering the characteristics of weak maneuverability and underactuation of it, the obstacle avoidance for the wave glider is challenging. In this paper, the dynamic obstacle avoidance strategy for the wave glider based on an improved artificial potential field (IAPF) is proposed. The IAPF is committed to addressing the local minimum in the traditional artificial potential field and the obstacle avoidance difficulties caused by the weak maneuverability of the wave glider. Various simulations are conducted to demonstrate the feasibility of the proposed strategy by adopting an eight-degree-of-freedom mathematical model of the wave glider. The simulation results show that the wave glider can accomplish the obstacle avoidance task with the proposed IAPF algorithm when facing with different dynamic obstacles under various marine environments.

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.

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 Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 31 ◽  
Author(s):  
Xiaohua Wang ◽  
Yan Liang ◽  
Shun Liu ◽  
Linfeng Xu
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Estimation Accuracy ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Model Free ◽  
Numeric Simulation

This paper presents the problem of obstacle avoidance with bearing-only measurements in the case that the obstacle motion is model-free, i.e., its acceleration is absolutely unknown, which cannot be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, the essential reason of the collision caused by local minimum problem in the standard artificial potential field method is proved, and hence a revised method with angle dependent factor is proposed. Then, an unknown input observer is proposed to estimate the position and velocity of the obstacle. Finally, the numeric simulation demonstrates the effectiveness in terms of estimation accuracy and terminative time.

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.

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