Techniques For Handling Difficult Lens Design Problems 1. The 'Local Minimum' Problem

1987 ◽  
Author(s):  
Michael J. Kidger
Keyword(s):  
Local Minimum ◽  
Minimum Problem ◽  
Lens Design ◽  
Design Problems ◽  
Local Minimum Problem

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.

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