The Null Space Based Cooperative Formation Control for UAVs Swarm

2013 ◽  
Vol 341-342 ◽  
pp. 824-829
Author(s):  
Shi You Dong ◽  
Xiao Ping Zhu ◽  
Guo Qing Long
Keyword(s):  
Control Strategy ◽  
Formation Control ◽  
Local Minimum ◽  
Null Space ◽  
Potential Functions ◽  
Good Effect ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Control Approach ◽  
Local Minimum Problem

In this paper, the formation problem of UAVs swarm is studied based on a combination of the potential functions. On the basis of mathematical models of the traditional artificial potential field,a new formation potential function is proposed. The potential functions is merged using null space control strategy which is capable of dealing with conflicts among elementary potential functions and avoid local minimum problem. The results achieved by computer simulations suggest that the control approach can produces good effect.

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.

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.

An Evolutionary Method of Traditional Artificial Potential Field

2012 ◽  
Vol 198-199 ◽  
pp. 1025-1029 ◽  
Author(s):  
Shun Hong Wang ◽  
Jiu Fen Zhao ◽  
Le Fei Pan ◽  
Xin Xue Liu ◽  
Bei Zhang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Local Minimum ◽  
Optimal Path ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Complex Environment ◽  
Evolutionary Method ◽  
Geometric Relationship ◽  
Relationship Of

The problems of goals non-reachable with obstacles nearby (GNRON) and dead lock caused by local minimum were described when using Artificial Potential Field (APF) methods for mobile robot path planning in complex environment. Considering the geometric relationship of different obstacles, the heuristic factor of virtual attractive forces was introduced to solve the local minimum problem. Simulation results indicate that the evolutionary method of APF can realize optimal path planning in complex environment, which makes up the disadvantages of traditional APF method.

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