Application of Null Space Based Behavior Control to the Swarm Robot’s Control
Affiliation(s)
Department of Cybernetics, University of Transport and Communications Hanoi, Hanoi, Vietnam.
Department of Cybernetics, University of Transport and Communications Hanoi, Hanoi, Vietnam.
ABSTRACT
This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provides the stability analysis of the converging process by investigating the relationship between single agents, and the analysis result is proved by using the Lyapunov theory. Finally, the simulation results in two-dimensional space have confirmed the obtained theoretical results.
This paper proposes a solution to controls warm robots in an effort to avoid obstacles, moving to the goal by the method of Null Space based Behavior (NSB) control of an individual in the swarm. This paper also provides the stability analysis of the converging process by investigating the relationship between single agents, and the analysis result is proved by using the Lyapunov theory. Finally, the simulation results in two-dimensional space have confirmed the obtained theoretical results.
Cite this paper
Nga, L. and Lan, L. (2015) Application of Null Space Based Behavior Control to the Swarm Robot’s Control. Modern Mechanical Engineering, 5, 97-104. doi: 10.4236/mme.2015.53009.
Nga, L. and Lan, L. (2015) Application of Null Space Based Behavior Control to the Swarm Robot’s Control. Modern Mechanical Engineering, 5, 97-104. doi: 10.4236/mme.2015.53009.
References
[1] Gazi, V. and Passino, K.M. (2002) Stability Analysis of Swarms. Proceedings of the American Control Conference Anchorage, 8-10 May 2002, 1813-1818.
http://dx.doi.org/10.1109/acc.2002.1023830 [2] Chen, X.-B., Pan, F.C., Li, L. and Fang, H. (2006) Practical Stability Analysis for Swarm Systems. IEEE Transactions on Automatic Control, 3904-3909. [3] Wang, L.S. and Fang, H.J. (2010) Stability Analysis of Practical Anisotropic Swarms. IEEE Transactions on Automatic Control, 768-772. [4] Smith, L.L., Venayagamoorth, G.K. and Holloway, P.G. (2012) Obstacle Avoidance in Collective Robotic Search Using Particle Swarm Optimization. IEEE Swarm Intelligence Symposium. [5] Bento, L.C., Pires, G. and Nunes, U. (2002) A Behavior Based Fuzzy Control Architecture for Path Tracking and Obstacle Avoidance. Proceedings of the 5th Portuguese Conference on Automatic Control, Aveiro, 341-346. [6] Antonelli, G. (2009) Stability Analysis for Prioritized Closed-Loop Inverse Kinematic Algorithms for Redundant Robotic Systems. IEEE Transactions on Robotics, 25, 985-994.
http://dx.doi.org/10.1109/TRO.2009.2017135 [7] Lan, L.H., Nga, L.T.T. and Lan, L.H. (2013) Aggregation Stability of Multiple Agents with Fuzzy Attraction and Repulsion Forces. MMAR, 81-85.
[1] Gazi, V. and Passino, K.M. (2002) Stability Analysis of Swarms. Proceedings of the American Control Conference Anchorage, 8-10 May 2002, 1813-1818.
http://dx.doi.org/10.1109/acc.2002.1023830 [2] Chen, X.-B., Pan, F.C., Li, L. and Fang, H. (2006) Practical Stability Analysis for Swarm Systems. IEEE Transactions on Automatic Control, 3904-3909. [3] Wang, L.S. and Fang, H.J. (2010) Stability Analysis of Practical Anisotropic Swarms. IEEE Transactions on Automatic Control, 768-772. [4] Smith, L.L., Venayagamoorth, G.K. and Holloway, P.G. (2012) Obstacle Avoidance in Collective Robotic Search Using Particle Swarm Optimization. IEEE Swarm Intelligence Symposium. [5] Bento, L.C., Pires, G. and Nunes, U. (2002) A Behavior Based Fuzzy Control Architecture for Path Tracking and Obstacle Avoidance. Proceedings of the 5th Portuguese Conference on Automatic Control, Aveiro, 341-346. [6] Antonelli, G. (2009) Stability Analysis for Prioritized Closed-Loop Inverse Kinematic Algorithms for Redundant Robotic Systems. IEEE Transactions on Robotics, 25, 985-994.
http://dx.doi.org/10.1109/TRO.2009.2017135 [7] Lan, L.H., Nga, L.T.T. and Lan, L.H. (2013) Aggregation Stability of Multiple Agents with Fuzzy Attraction and Repulsion Forces. MMAR, 81-85.