scholarly journals Analysis of Obstacle Avoidance Strategy for Dual-Arm Robot Based on Speed Field with Improved Artificial Potential Field Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1850
Author(s):  
Hui Zhang ◽  
Yongfei Zhu ◽  
Xuefei Liu ◽  
Xiangrong Xu
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Avoidance Strategy ◽  
Dual Arm Robot ◽  
Dual Arm ◽  
Avoidance Problem

In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.

Design Research on Vehicle Collision Avoidance Based on Artificial Potential Field

2012 ◽  
Vol 271-272 ◽  
pp. 727-731 ◽  
Author(s):  
Ying Jie Liu ◽  
You Qun Zhao ◽  
Xiao Feng Zhou
Keyword(s):  
Collision Avoidance ◽  
Potential Function ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Design Research ◽  
Driving Safety ◽  
Artificial Potential Field ◽  
The Road ◽  
Vehicle Collision ◽  
Avoidance Problem

Vehicle driving safety is the urgent key problem to be solved of automobile independent development while encountering collision avoidance. It is also the premise and one of the necessary conditions of vehicle active safety. A new technique for vehicle collision avoidance was proposed. Based on the artificial potential field theory, the lane potential, the road potential function and the obstacle potential function as well as the velocity potential function of the vehicle were constructed. Then the potential function of the vehicle obstacle avoidance problem was constructed with the three potential functions above. The vehicle obstacle avoidance problem was then converted into an optimization problem. The trajectory of the vehicle in the obstacle avoidance process was obtained by solving the optimal control problem. The simulation results show that the proposed method can solve the collision avoidance problem and provide the lane keeping and lane change problem with theoretical support

scholarly journals Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

2021 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Xun Yan ◽  
Dapeng Jiang ◽  
Runlong Miao ◽  
Yulong Li
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Shape Change ◽  
Artificial Potential Field ◽  
Generation Algorithm ◽  
Time Performance ◽  
Movement Ability ◽  
Window Approach ◽  
Avoidance Problem

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals Obstacle Avoidace Robot Using LabView

2015 ◽  
Vol 4 (3) ◽  
pp. 164
Author(s):  
Tasher Ali Sheikh ◽  
Swacheta Dutta ◽  
Smriti Baruah ◽  
Pooja Sharma ◽  
Sahadev Roy
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Autonomous Robotics ◽  
Continuous Transition ◽  
Heavy Burden ◽  
Avoidance Strategy ◽  
Small Period ◽  
Specific Amount

The concept of path planning and collision avoidance are two of the most common theories applied for designing and developing in advanced autonomous robotics applications. NI LabView makes it possible to implement real-time processor for obstacle avoidance. The obstacle avoidance strategy ensures that the robot whenever senses the obstacle stops without being collided and moves freely when path is free, but sometimes there exists a probability that once the path is found free and the robot starts moving, then within a fraction of milliseconds, the robot again sense the obstacle and it stops. This continuous swing of stop and run within a very small period of time may cause heavy burden on the system leading to malfunctioning of the components of the system. This paper deals with overcoming this drawback in a way that even after the robot calculates the path is free then also it will wait for a specific amount of time before running it. So as to confirm that if again the sensor detects the obstacle within that specified period then robot don’t need to transit its state suddenly thus avoiding continuous transition of run and stop. Thus it reduces the heavy burden on the system.

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 Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

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.

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