Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

SummaryThis paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.

Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems Approach

This paper introduces a decentralized approach of collaborative control between multiple robots. A dynamic problem is considered to illustrate the effectiveness of this approach. The objective of this problem is to control three robots that are connected to a ball through elastic strings to bring the ball to a pre-defined target position. Since there is no communication between the robots, each robot does not know how the other robots are going to react at any instant. The only information available to the robots are the current and target positions of the ball. Genetic Fuzzy Systems (GFSs) are used to develop controllers for individual robots to tackle this problem. The nonlinearity of fuzzy logic systems coupled with the search capability of Genetic Algorithm (GA) provides an invaluable tool to design controllers for such tasks. The system is first trained through a set of scenarios and then applied to an extensive test set to test the effectiveness of the approach.

Genetic Fuzzy System for Financial Management

This paper discusses genetic fuzzy systems – hybrid systems of artificial intelligence combining the potential of fuzzy sets for modeling approximate reasoning with the abilities of genetic algorithms for finding optimal solutions. The use of genetic algorithms for optimizing the parameters of a fuzzy system is demonstrated on GFSSAM.