scholarly journals Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment

2019 ◽  
Vol 9 (8) ◽  
pp. 1589 ◽  
Author(s):  
Jiubo Sun ◽  
Guoliang Liu ◽  
Guohui Tian ◽  
Jianhua Zhang
Keyword(s):  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Moving Object ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Field Approach ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Local Minimum Problem

The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.

scholarly journals Improved Artificial Potential Field and Dynamic Window Method for Amphibious Robot Fish Path Planning

2021 ◽  
Vol 11 (5) ◽  
pp. 2114
Author(s):  
Wenlin Yang ◽  
Peng Wu ◽  
Xiaoqi Zhou ◽  
Haoliang Lv ◽  
Xiaokai Liu ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Artificial Potential Field ◽  
Evaluation Function ◽  
Potential Field Method ◽  
Robot Fish ◽  
Amphibious Robot ◽  
Artificial Potential Field Method

Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.

Intelligent Vehicle Path Planning Based on Improved Artificial Potential Field Method

2015 ◽  
Vol 742 ◽  
pp. 349-354 ◽  
Author(s):  
Zhou Pan ◽  
Jia Qi Li ◽  
Kai Min Hu ◽  
Hao Zhu
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Fuzzy Controller ◽  
Field Method ◽  
Intelligent Vehicle ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Potential Field Method ◽  
Vehicle Path ◽  
Artificial Potential Field Method

Aiming at the path planning for intelligent vehicle in complex environment, local minimum problem is solved by the way of setting a virtual barrier point. And fuzzy controller is designed to make up some inherent shortcomings of artificial potential field method and safeguards the reliability of the path planning and path smoothness.

scholarly journals 3D Path Planning Method for UAV Based on Improved Artificial Potential Field

2020 ◽  
Vol 38 (5) ◽  
pp. 977-986
Author(s):  
Yicong Guo ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Yue Yang
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Artificial Potential Field ◽  
Field Calculation ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.

Predictive Artificial Potential Field Method and its Application on Robot Soccer Path Planning

2011 ◽  
Vol 48-49 ◽  
pp. 840-843 ◽  
Author(s):  
Peng Huang ◽  
Chang Yun Miao ◽  
Li Jin Guo ◽  
Ying Li
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Robot Soccer ◽  
Uncertain Environment ◽  
Field Approach ◽  
Potential Field Method ◽  
Artificial Potential Field Method

This paper presents a new predictive artificial potential field approach for robot soccer path planning under complex and uncertain environment. By predicting and analyzing the future position and attitude of concerned object, the position and attitude of the object is controlled by demonstration algorithm. The proposed method is successfully used in the robor soccer shooting and is realized on the MiroSot 3vs3 simulating platform. Experiment results show that this algorithm has good real-time ability and adaptability to environment.

An Optimization Path Planning Method Based on Evolutionary Artificial Potential Field

2013 ◽  
Vol 380-384 ◽  
pp. 1414-1417
Author(s):  
Fei Long Li
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Local Minimum ◽  
Minimum Point ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Field Approach ◽  
Local Minimum Point ◽  
The Matrix ◽  
Local Minimum Problem

This paper presents an evolutionary way for the robot to plan path. The way is based on the Evolutionary Artificial Potential Field approach. APF is an efficient way for a robot to plan its path, and the evolutionary APF can help the robot to jump out of the local minimum point. A matrix is integrated in the new algorithm. The matrix can modify the direction of a robot when the robot is trapped in a local minimum point. The force which has been changed will prompt the robot to escape from the local minimum point. Simulation result shows that the optimized algorithm is an effective way to solve the local minimum problem.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

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