Autonomous Generation of the Potential Field on Obstacle 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.

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Design Research on Vehicle Collision Avoidance Based on Artificial Potential Field

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.271-272.727 ◽

2012 ◽

Vol 271-272 ◽

pp. 727-731 ◽

Cited By ~ 2

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

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Autonomous Generation of Potential Field in Obstacle Avoidance Problem

Journal of Robotics and Mechatronics ◽

10.20965/jrm.1992.p0307 ◽

1992 ◽

Vol 4 (4) ◽

pp. 307-313

Author(s):

Shuichi Yokoyama ◽

◽

Masafumi Uchida ◽

Kei Fukushima ◽

Keyword(s):

Obstacle Avoidance ◽

Potential Field ◽

Avoidance Problem

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Research on Multi-Agent Formation’s Obstacle Avoidance Problem Based on Three-Dimensional Vectorial Artificial Potential Field Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.251 ◽

2014 ◽

Vol 596 ◽

pp. 251-258 ◽

Cited By ~ 3

Author(s):

Ji Yang Dai ◽

Lin Fei Yin ◽

Chen Peng ◽

Bao Jian Yang ◽

Cun Song Wang

Keyword(s):

Obstacle Avoidance ◽

Potential Field ◽

Three Dimensional ◽

Field Method ◽

Artificial Potential Field ◽

Potential Field Method ◽

Pyramid Structure ◽

Multi Agent ◽

Artificial Potential Field Method ◽

Avoidance Problem

In order to solve the obstacle avoidance problem when the Multi-Agent formation get through the area full of obstacles, improved the traditional Artificial Potential Field method, add the vectorial information to the agent’s model, presented the Three-Dimensional Vectorial Artificial Potential Field method (TDVAPF). Firstly, improved the model of agent, obstacle and target; then, improved the Multi-Agent formation motion model, the Multi-Agent formation’s structure is “pyramid” structure; Finally, improved the agent’s force, add the “rotational force” to the agent’s force, it makes agent avoid the “local trouble”. The numerical simulation verified the correctness and effectiveness of the TDVAPF method in Multi-Agent formation’s obstacle avoidance problem.

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

Electronics ◽

10.3390/electronics10151850 ◽

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.

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A Study on information Processing using the Vibrating Potential Field. An Approach to the Obstacle-Avoidance Problem.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.59.2346 ◽

1993 ◽

Vol 59 (564) ◽

pp. 2346-2352

Author(s):

Hiroshi Yokoi ◽

Yukinori Kakazu

Keyword(s):

Information Processing ◽

Obstacle Avoidance ◽

Potential Field ◽

Avoidance Problem

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

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211026449 ◽

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.

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