۱- Introduction

۲- Related work

۳- Research objectives

۴- Distance based formation using GA

۵- Discussion

۶- Conclusions

References

Abstract

In this paper an alternative method to achieve distance based formation is presented. The method uses Genetic Algorithms to find a suitable solution based on angle and distance, and an appropriate constant velocity to avoid collisions. The designed algorithm is extended to a parallel scheme to improve its performance and achieve Artificial Distributed Intelligence, in which the robots share, through solution migration, the best ways to converge to desired distances while avoiding collisions, finally reaching consensus on the solution. The algorithm is tested using simulations and real robots experiments.

Introduction

Researchers of the fast growing field of Multi robot systems (MRS) focus on the idea that there is strength in numbers, as presented in Dudek’s example [1] of a task that requires simultaneous operation where the restrictions of a single robot system is overcome by a MRS. MRS made out of a large number of simple and cheap robots could have better performance than individual highly specialized and costly robots, mainly because: with more robots exploration of a wider spatial area can be faster and distributed specialized jobs can be carry out by different team members if the MRS is heterogeneous. Another important characteristic of MRS is redundancy, a large number of robots support each other carrying out teammates task if the robots fails to achieve the mission [2]. There is a lot of research in MRS on designing appropriate coordination strategies and control laws that enable them to achieve objectives efficiently together [3–۵]. Coordination strategies include various problems that aim to drive a group of agents to some common state, this is usually called consensus, agreement, synchronization or rendezvous, in this paper this strategies are referred as the consensus problem, extensively studied in [6,7], modelling robots with single integrator and using graph theory describe topology, ergo, connections between robots. Control laws are designed to achieve consensus avoiding inter robot interference [8]. Global convergence, group stability, group following and other characteristics are studied through their topology graphs [2].