A Cooperative Hunting Algorithm of Multi-robot Based on Dynamic Prediction of the Target via Consensus-based Kalman Filtering

2015 ◽  
Vol 12 (4) ◽  
pp. 1557-1568
Author(s):  
Shiming Chen
Keyword(s):  
Kalman Filtering ◽  
Dynamic Prediction ◽  
Cooperative Hunting ◽  
Multi Robot

scholarly journals A novel bio- inspired algorithm for hunting in multi robot scenario

2019 ◽  
Vol 15 (3) ◽  
pp. 1553
Author(s):  
Poorva Agrawal ◽  
Himanshu Agrawal ◽  
Vidyasagar Potdar
Keyword(s):  
Dynamic Alliance ◽  
Construction Algorithm ◽  
Cooperative Hunting ◽  
Four Corners ◽  
Different Shapes ◽  
Multi Robot

In a multi-robot scenario, cooperative hunting is a key issue when a group of robots are hunting for evader/evaders and when the location of the evader is continuously changing. Cooperative hunting is addressed in this paper by proposing a novel bio inspired Corner Dragging Algorithm (CDA). Corner Dragging Algorithm operates by making an alliance of robots that drag the evader towards any one of the four corners; whichever is closest to the evader. Different shapes of obstacles are avoided during this pursuit. While developing the Corner Dragging Algorithm, we analyze the shortcomings and advantages of some of the existing algorithms including dynamic alliance and formation construction algorithm and incorporate these changes in our design to achieve improved results. Performance of the algorithm is evaluated on the basis of simulation in MATLAB.

scholarly journals Hunting strategy for multi-robot based on wolf swarm algorithm and artificial potential field

2022 ◽  
Vol 25 (1) ◽  
pp. 159
Author(s):  
Oussama Hamed ◽  
Mohamed Hamlich ◽  
Mohamed Ennaji
Keyword(s):  
Potential Field ◽  
Artificial Potential Field ◽  
Moving Target ◽  
Robot System ◽  
Cooperative Hunting ◽  
Robot Systems ◽  
Dynamic Target ◽  
Swarm Algorithm ◽  
Hunting Strategy ◽  
Multi Robot

The cooperation and coordination in multi-robot systems is a popular topic in the field of robotics and artificial intelligence, thanks to its important role in solving problems that are better solved by several robots compared to a single robot. Cooperative hunting is one of the important problems that exist in many areas such as military and industry, requiring cooperation between robots in order to accomplish the hunting process effectively. This paper proposed a cooperative hunting strategy for a multi-robot system based on wolf swarm algorithm (WSA) and artificial potential field (APF) in order to hunt by several robots a dynamic target whose behavior is unexpected. The formation of the robots within the multi-robot system contains three types of roles: the leader, the follower, and the antagonist. Each role is characterized by a different cognitive behavior. The robots arrive at the hunting point accurately and rapidly while avoiding static and dynamic obstacles through the artificial potential field algorithm to hunt the moving target. Simulation results are given in this paper to demonstrate the validity and the effectiveness of the proposed strategy.

scholarly journals Dynamic Prediction of Financial Distress Based on Kalman Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qian Zhuang ◽  
Lianghua Chen
Keyword(s):  
Kalman Filtering ◽  
Financial Distress ◽  
Process Model ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Prediction Algorithm ◽  
Dynamic Prediction ◽  
Discriminant Model ◽  
Financial Distress Prediction ◽  
Distress Prediction

The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.

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