A Robotic Swarm Searching Method for Unknown Environments Based on Foraging Behaviors

Abstract:

This paper proposes a novel method for a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics that are inspired by the foraging behavior in nature. First, the searching area is divided into several sub-regions using a target utility function, from which each robot can identify an area that should be initially searched. Then, a predatory strategy is used for searching in the sub-regions; this hybrid approach integrates a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a robot cannot find any target information in the sub-region, it uses a global random search algorithm; if the robot finds any target information in the sub-region, the DPSO search algorithm is used for a local search. The particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism as the searching progresses until the robots find the target position. Then, the robots fall back to a random searching mode and continue to search for other places that were not searched previously. In this searching strategy, the robots alternate between two searching algorithms until the whole sub-area is covered. During the searching process, the robots use a local communication mechanism to share map information and the DPSO parameters to reduce the communication burden and overcome hardware limitations.

• Periodical: Applied Mechanics and Materials (Volume 461)
• Pages: 853-860
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.461.853
• Online since: November 2013
• Authors: Shou Tao Li, Li Na Li, Gordon Lee
• Keywords: Hybrid Search Algorithm, Local Communication, Target Utility Function

References

[1] C. Luo, A.P. Espinosa, D. Pranantha, and A. De Gloria, A. Multi-Robot Search and Rescue Team, Proc. of IEEE International Symposium on Safety, Security and Rescue Robotics, Kyoto, (2011) 296–301.

DOI: 10.1109/ssrr.2011.6106746

[2] M. Couceiro, R. Rocha, and N. Ferreira, A Novel Multi-Robot Exploration Approach Based on Particle Swarm Optimization Algorithms, Proc. of IEEE International Symposium on Safety, Security and Rescue Robotics, Kyoto , (2011) 327–332.

DOI: 10.1109/ssrr.2011.6106751

[3] J. de Hoog, S. Cameron, and A. Visser, Dynamic Team Hierarchies in Communication-Limited Multi-Robot Exploration, Proc. of IEEE International Workshop on Safety Security and Rescue Robotics, Bremen, (2010) 1–7.

DOI: 10.1109/ssrr.2010.5981573

[4] K. Zhang, E.G. Collins Jr., and D. Shi, Centralized and Distributed Task Allocation in Multi-Robot Teams Via a Stochastic Clustering Auction. ACM Transactions On Autonomous and Adaptive Systems, Vol. 7, No. 2, Article 21, (2012).

DOI: 10.1145/2240166.2240171

[5] A. Sagoff, R.J. Wood, and R. Nagpal, Two Foraging Algorithms for Robot Swarms using Only Local Communication, Proc. of the International Conference on Robotics and Biomimetics, Tianjin, (2010) 123-130.

DOI: 10.1109/robio.2010.5723314

[6] A. Darvishzadeh, Distributed Multi-Robot Collaboration Using Evolutionary Computation, MS Thesis, University of California, Riverside, (2011).

[7] I. Sisso, T. Shima, and Y. Ben-Haim, Info-Gap Approach to Multi-agent Search Under Severe Uncertainty, IEEE Transactions on Robotics, Vol. 26, No. 6, Dec. (2010).

DOI: 10.1109/tro.2010.2073050

[8] C.H. Chang, J.T. Zou, and K.L. Su, K., Target Searching of Mobile Robots Using Improved A* Searching Algorithm, Applied Mechanics and Materials, Vol. 300-301, (2013) 519-522.

DOI: 10.4028/www.scientific.net/amm.300-301.519

[9] K. Sundareswaran and V.T. Sreedevi, Development of Novel Optimization Procedure Based on Honey Bee Foraging Behavior, Proc. of the IEEE International Conference on Systems, Man and Cybernetics, (2008) 1220-1225.

DOI: 10.1109/icsmc.2008.4811449

[10] E. Bonabeau and G. Theraulaz, Swarm Smarts, Scientific American, (March 2000) 73-39.

[11] M.L. Winston, The Biology of Honey Bees, Harward University Press, Cambridge, MA., (1987).