Designing an Optimal Stable Algorithm for Robot Swarm Motion toward a Target

Document Type: Original Article

Authors

1 department of Mechanical Engineering, Pardis branch, Islamic Azad University, Tehran

2 department of aerospace engineering, science and research branch, Azad university, Tehran, Iran

3 faculty of Aerospace engineering, Science and Research Branch, Islamic Azad University, Tehran

Abstract

In this paper, an optimal stable algorithm is presented for members of a robots swarm moving toward a target. Equations of motion of the swarm are based on Lagrangian energy equations. Regarding of similar research On the design of swarm motion algorithm, an equation of motion considered constraints to guarantee no collision between the members and the members and obstacles along the motion path is presented. In order to optimize the swarm motion stability algorithm, the required constraints are introduced into the equations of motion in the form of a potential function. Considering Points of various coordinates on a target and applied potential function, each member is guided to the closest point using their coordinates while communicating with others from the beginning of the swarm motion. As a result, the need for having the members gathered within an area close to the center of the swarm observed in previously designed algorithms is eliminated. The designed optimal stability algorithm is simulated in MATLAB Software for a swarm composed of two robots under different sets of conditions. Simulation results of swarm member behavior were indicative of reducing mission time with increasing motion space for the swarm members while optimizing the behavior of the swarm moving toward the target. Finally, some experimental results related to designed algorithm are presented.

Keywords

References

[1] G. Flake, The Computational Beauty of Natur, Cambridge University, MIT Press, (1999).
[2] S. Kazadi, Swarm engineering, Ph.D. thesis, California Institute of Technology, (2000).
[3] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotics Systems. NATO Advanced Workshop on Robots and Biological System, (1989).
[4] C.W. Reynolds, Flocks, herds, and schools: A distributed behavioural model, Comp, ACM SIGGRAPH computer graphics, California (1987).
 [5]        A. Kushleyev, D. Mellinger, V. Kumar, Towards A Swarm of Agile Micro Quadrotor, GRASP Lab, University of Pennsylvania, USA, (2013).
[6] A.M.  Naghsh, A. Tanoto, Analysis and design of human-robot swarm interaction in firefighting, The 17th IEEE International Symposium on Robot and Human Interactive Communication, (2008).
[7] V. Gazi, Stability Analysis of Swarms, PhD thesis, Ohio State University, USA, (2002).
[8] A .Mong, S. Loizou, Stabilization of Multiple Robots on Stable Orbits via Local Sensing. IEEE International Conference on Robotics and Automation, (2007).
[9] H. Hashimoto, S. Aso, S. Yokota, A. Sasaki, Cooperative Movement of Human and Swarm Robot Maintaining  Stability of Swarm. The 17th IEEE International Symposium , (2008).
[10] V. Gazi. On Lagrangian Dynamics Based Modelling of Swarm Behaviour. Department of Electrical and Electronics Engineering, Istanbul Kemerburgaz University, Turkey,  (2013).
[11] A. Ghafari, A. Khodayari, A. Poormahmoodi, Providing an algorithm based on unwillingness to accumulate in the two-dimensional movements of Swarm robots, 24th Annual International Conference on Mechanical Engineering, Iran, (2016).
[12] A. Poormahmoodi, A. Ghaffari, A. Khodayari, Stability pattern of Movements for Swarm Robots. M.Cs  thesis, South Tehran Branch, Islamic Azad University, Iran,  (2016).
[13] Z. Chen, H. Liao, T. Chu, Aggregation and Splitting in Self-Driven Swarms. Phisica A, Elsevier, (2012).
[14] M. Brambilla, E. Frante, M. Birattari, Swarm Robotics: a Review From the Swarm Engineering Prespective. Springer. Swarm Intell,  (2013).
[15] S. Chui, X. Wang, J. Geng, Intelligent Swarm Analysis on Aggregation Based Optimal Fuzzy Controller. Controll and Decision Conference , IEEE, China, (2008).
[16] J. Rothermich, I. Ecemiş, P. Gaudiano, Distributed Localization and Mapping with a Robotic Swarm. Springer Berlin Heidelberg, Germany, (2004).
[17] V. Kumar, F. Sahin, Cognitive Maps in Swarm Robots for the Mine Detection Application. Systems, Man and Cybernetics, IEEE, (2003).
[18] J. Kim, J. Wook, J. Seo, Mapping and Path Planning Using Communication graph of Unlocalized and Randomly Deployed Robotic Swarm. Control, Automation and Systems (ICCAS), (2016).
[19] A. Khodayari, A. Ghafari, H. Khodayari, Design, construction and validation of a quadrotor with the aim of using as a swarm member, 25th Conference of Mechanical Engineering, Tarbiat Modarres University, Iran, (2017).
[20] H. Khodayari, Designing of an Optimal Fuzzy Controller Using Linear Quadratic Regulator Method for a Quad-Rotor. MSc thesis, Department of aerospace engineering, Science and Research branch, Azad University, Iran, (2013).
[21] F. Pazooki, H. Khodayari, Attitude stability optimization of a quadrotor with a fuzzy controller. 13th conference of aerospace community of Iran, Tehran University, Iran, (2014).
[22] H. Khodayari, F. Pazooki, Designing an optimal fuzzy controller with LQR method for controlling the attitude of quadrotor. The International Society of Mechanical Engineering Conference (ISME), Iran, (2014).
International Journal of Robotics, Theory and Applications

Volume 5, Issue 1
Spring 2019
Pages 27-34

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