Swarm robotics has emerged as a promising field within the realm of artificial intelligence and robotics, focusing on the coordination and control of large groups of simple, inexpensive robots. This paper explores the principles, challenges, and strategies involved in designing multi-robot systems that can effectively collaborate and adapt to dynamic environments. The abstract delves into the fundamental concepts of swarm intelligence, where individual robots operate autonomously while following collective rules to achieve a common goal. We discuss various coordination mechanisms, such as local communication, consensus algorithms, and distributed control, that enable robots to maintain formation, share information, and respond to changes in their environment. Furthermore, we analyze the control strategies employed to ensure task allocation, optimize performance, and manage the robot swarm's energy consumption. The paper concludes with a discussion on the future directions of swarm robotics research and its potential applications in fields such as environmental monitoring, search and rescue operations, and manufacturing.
Johnson, E. Swarm Robotics: Coordination and Control Strategies in Multi-Robot Systems. Transactions on Applied Soft Computing, 2023, 5, 45. https://doi.org/10.69610/j.tasc.20231222
AMA Style
Johnson E. Swarm Robotics: Coordination and Control Strategies in Multi-Robot Systems. Transactions on Applied Soft Computing; 2023, 5(2):45. https://doi.org/10.69610/j.tasc.20231222
Chicago/Turabian Style
Johnson, Emily 2023. "Swarm Robotics: Coordination and Control Strategies in Multi-Robot Systems" Transactions on Applied Soft Computing 5, no.2:45. https://doi.org/10.69610/j.tasc.20231222
APA style
Johnson, E. (2023). Swarm Robotics: Coordination and Control Strategies in Multi-Robot Systems. Transactions on Applied Soft Computing, 5(2), 45. https://doi.org/10.69610/j.tasc.20231222
Article Metrics
Article Access Statistics
References
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Oxford University Press.
Maes, P. (1994). Learning and adapting in the distributed artificial intelligence community. Communications of the ACM, 37(6), 38-53.
Deneubourg, J. L., & Gossage, J. M. (1990). Social insect simulation: An approach towards collective robotics. In Proceedings of the 1990 IEEE International Conference on Robotics and Automation (ICRA) (Vol. 4, pp. 2757-2762).
Jadbabaie, M., Lin, J., & Morse, A. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 48(6), 988-1001.
Olfati-Saber, R., Faxena, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215-233.
Focchi, M., Nardi, D., & Scattolini, R. (2005). Flocking and formation control of multi-agent systems. In Proceedings of the 2005 American Control Conference (pp. 4623-4628).
Li, J., Wang, C., Zhang, Y., & Liu, Z. (2013). Multi-agent reinforcement learning for distributed task allocation in multi-robot systems. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation (pp. 1290-1295).
Wang, H., Li, Y., & Li, Z. (2011). Guidance and formation control of multi-agent systems with nonholonomic constraints. In Proceedings of the 2011 American Control Conference (pp. 3387-3392).
Zhang, G., & Li, Y. (2010). Adaptive control for formation reconfiguration of multi-agent systems. In Proceedings of the 2010 American Control Conference (pp. 1813-1818).
Erbaser, A., Montanari, A., & Scattolini, R. (2006). A graph-based approach to task allocation in multi-robot systems. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA) (Vol. 4, pp. 3973-3978).
Su, Y., Liu, Z., & Wang, C. (2013). Optimization-based task allocation in multi-robot systems with workload constraints. In Proceedings of the 2013 American Control Conference (pp. 1307-1312).
Cao, M., Wang, C., & Liu, Z. (2012). Energy-aware task allocation for heterogeneous multi-robot systems. In Proceedings of the 2012 American Control Conference (pp. 6217-6222).
Wang, C., Li, Y., & Liu, Z. (2011). Adaptive energy management for multi-robot systems based on a hybrid particle swarm optimization approach. In Proceedings of the 2011 American Control Conference (pp. 6206-6211).