Standard particle swarm optimization on source seeking using mobile robots

56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

Volumn , Issue , 2015, Pages

  Zou, Rui ^a   Kalivarapu, Vijay ^b   Bhattacharya, Sourabh ^b   Winer, Eliot ^b   Oliver, James ^b  

^a IOWA STATE UNIVERSITY   (United States)

^b Iowa State University of Science and Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MOBILE ROBOTS; STRUCTURAL DYNAMICS; SWARM INTELLIGENCE;

COMPLEX ENVIRONMENTS; GRADIENT BASED; NON-DIFFERENTIABLE; OPTIMIZATION METHOD; PHYSICAL LIMITATIONS; ROBOTIC IMPLEMENTATION; SIGNAL SOURCE; TOPOLOGY MODEL;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 85088193562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2514/6.2015-0897     Document Type: Conference Paper

References (33)

1. S. Pang and J. Farrell, “Chemical plume source localization,” Part B: Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics, vol. 36, no. 5, pp. 1068-1080, 2006.
2. A. Wadhwa, U. Madhow, J. Hespanha, and B. Sadler, “Following an rf trail to its source,” in 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 580-587, 2011.
3. B. Charrow, N. Michael, and V. Kumar, “Cooperative multi-robot estimation and control for radio source localization,” in Experimental Robotics, vol. 88 of Springer Tracts in Advanced Robotics, pp. 337-351, Springer International Publishing, 2013.
4. P. Ogren, E. Fiorelli, and N. Leonard, “Cooperative control of mobile sensor networks:adaptive gradient climbing in a distributed environment,” IEEE Transactions on Automatic Control, vol. 49, no. 8, pp. 1292-1302, 2013.
5. N. Atanasov, J. Le Ny, N. Michael, and G. Pappas, “Stochastic source seeking in complex environments,” in IEEE International Conference on Robotics and Automation (ICRA), pp. 3013-3018, 2012.
6. E. Burian, D. Yoerger, A. Bradley, and H. Singh, “Gradient search with autonomous underwater vehicles using scalar measurements,” in Proceedings of the 1996 Symposium on Autonomous Underwater Vehicle Technology, pp. 86-98, 1996.
7. S. Venkateswaran, J. Isaacs, K. Fregene, R. Ratmansky, B. Sadler, J. Hespanha, and U. Madhow, “Rf source-seeking by a micro aerial vehicle using rotation-based angle of arrival estimates,” in American Control Conference (ACC), 2013, pp. 2581-2587, 2013.
8. W. Wu and F. Zhang, “Robust cooperative exploration with a switching strategy,” IEEE Transactions on Robotics, vol. 28, no. 4, pp. 828-839, 2012.
9. R. Bachmayer and N. Leonard, “Vehicle networks for gradient descent in a sampled environment,” in Proceedings of the 41st IEEE Conference on Decision and Control, vol. 1, pp. 112-117 vol.1, 2002.
10. B. Moore and C. Canudas-de-Wit, “Source seeking via collaborative measurements by a circular formation of agents,” in American Control Conference (ACC), 2010, pp. 6417-6422, 2010.
11. S. Zhu, D. Wang, and C. Low, “Cooperative control of multiple uavs for moving source seeking,” Journal of Intelligent & Robotic Systems, vol. 74, no. 1-2, pp. 333-346, 2014.
12. C. Zhang, D. Arnold, N. Ghods, A. Siranosian, and M. Krstic, “Source seeking with nonholonomic unicycle without position measurement—part i: Tuning of forward velocity,” in 45th IEEE Conference on Decision and Control, pp. 3040-3045, 2006.
13. J. Cochran and M. Krstic, “Nonholonomic source seeking with tuning of angular velocity,” IEEE Transactions on Automatic Control, vol. 54, no. 4, pp. 717-731, 2009.
14. K. B. Ariyur and M. Krstic, Real-Time Optimization by Extremum-Seeking Control. Wiley-Interscience, 2003.
15. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in IEEE International Conference on Neural Networks, 1995. Proceedings.,, vol. 4, pp. 1942-1948 vol.4, 1995.
16. M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002.
17. K. Veeramachaneni, T. Peram, C. Mohan, and L. Osadciw, “Optimization using particle swarms with near neighbor interactions,” in Genetic and Evolutionary Computation GECCO 2003, vol. 2723, pp. 110-121, Springer Berlin Heidelberg, 2003.
18. M. Pant, R. Thangaraj, and A. Abraham, “A new quantum behaved particle swarm optimization,” in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO ’08, (New York, NY, USA), pp. 87-94, ACM, 2008.
19. V. Kalivarapu, J.-L. Foo, and E. Winer, “Improving solution characteristics of particle swarm optimization using digital pheromones,” Structural and Multidisciplinary Optimization, vol. 37, no. 4, pp. 415-427, 2009.
20. “Particle swarm central,” May 2014. https://www.particleswarm.info.
21. D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, pp. 120-127, April 2007.
22. M. Zambrano-Bigiarini, M. Clerc, and R. Rojas, “Standard particle swarm optimisation 2011 at cec-2013: A baseline for future pso improvements,” in 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2337-2344, June 2013.
23. K. Guruprasad and D. Ghose, “Automated multi-agent search using centroidal voronoi configuration,” IEEE Transactions on Automation Science and Engineering, vol. 8, no. 2, pp. 420-423, 2011.
24. A. R. Mesquita, J. P. Hespanha, and K. Astrom, “Optimotaxis: A stochastic multi-agent optimization procedure with point measurements,” in Hybrid Systems: Computation and Control, 11th International Workshop, HSCC 2008, vol. 4981, pp. 358-371, 2008.
25. L. Albini, P. Burrascano, and S. Fiori, “A feasibility study for electromagnetic pollution monitoring by electromagnetic- source localization via neural independent component analysis,” Neurocomputing, vol. 55, no. 34, pp. 451-468, 2003.
26. Y. Landa, N. Tanushev, and R. Tsai, “Discovery of point sources in the helmholtz equation posed in unknown domains with obstacles,” Comm. in Math. Sci., vol. 9, no. 3, pp. 903-928, 2011.
27. A. Khanafer, S. Bhattacharya, and T. Basar, “Adaptive resource allocation in jamming teams using game theory,” in 7th International workshop on Resource Allocation and Cooperation in Wireless Networks(RAWNET), 2011.
28. R. Zou, V. Kalivarapu, J. Oliver, and S. Bhattacharya, “Swarm optimization techniques for multi-agent source localiza­tion,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 402-407, July 2014.
29. R. Zou, M. Zhang, V. Kalivarapu, E. Winer, and S. Bhattacharya, “Particle swarm optimization for source localization in environment with obstacles,” in IEEE Multi-Conference on Systems and Control, October 2014. To Appear.
30. M. Clerc, Particle Swarm Optimization. ISTE, Wiley, 2010.
31. V. J. Lumelsky and A. Stepanov, “Dynamic path planning for a mobile automaton with limited information on the environment,” Automatic Control, IEEE Transactions on, vol. 31, pp. 1058-1063, Nov 1986.
32. H. Choset, Principles of Robot Motion: Theory, Algorithms, and Implementation. A Bradford book, Prentice Hall of India, 2005.
33. E. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269-271, 1959.