Abstract
In this paper, optimization-based approach has been adopted to localize the odor source in an unknown environment. Two scenarios taken into consideration, first single odor source (SOS) with a point source emission at a constant rate and four multiple odor sources (MOS) with point source emissions and different release rates constant in time. In context to SOS, four environments that have distinct dimensional layout have been generated with slight variation in wind velocity and diffusion constant. In case of MOS, there are five environments with same layout but different contributing factors such as wind velocity, placement of odor sources and emission rates which are considered to demonstrate its impact on success rate of algorithms. A recent optimization technique called hybrid teaching learning particle swarm optimization (HTLPSO) has been adopted and implemented in all the arenas, namely SOS and MOS, where mobile robots AKA virtual agents (VAs) are working in collaboration. There are group of VAs deployed in this operation ranging from {3–15}. To investigate the effectiveness of the algorithm, results of HTLPSO are compared with classical particle swarm optimization (PSO) and teaching learning-based optimization (TLBO). It is observed that HTLPSO outperforms TLBO and PSO in arenas with larger dimensions while utilizing few iterations in comparison with other algorithms in case of SOS. HTLPSO also performs best in case of MOS, surviving the effect of wind velocity and change in emission rates. Only when odor sources are placed differently and scattered, TLBO gives the best result. Another highlight of HTLPSO is convergence with high accuracy even with less number of VAs.
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References
Chen Y, Cai H, Chen Z and Feng Q 2017 Using multi-robot active olfaction method to locate time-varying contaminant source in indoor environment. Build. Environ. 118: 101–112
Hutchinson M, Oh H and Chen W-H 2017 A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Inf. Fusion 36: 130–148
Kowadlo G and Russell R A 2008 Robot odor localization: a taxonomy and survey. Int. J. Robot. Res. 27(8): 869–894
Marjovi A and Marques L 2013 Optimal spatial formation of swarm robotic gas sensors in odor plume finding. Auton. Robots 35(2–3): 93–109
Pasternak Z, Bartumeus F and Grasso F W 2009 Lévy-taxis: a novel search strategy for finding odor plumes in turbulent flow-dominated environments. J. Phys. A Math. Theor. 42(43): 434010
Yuan J, Oswald D and Li W 2015 Autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments using Petri nets. Expert Syst. Appl. 42(1): 527–538
Li W 2010 Identifying an odour source in fluid-advected environments, algorithms abstracted from moth-inspired plume tracing strategies. Appl. Bion. Biomech. 7(1): 3–17
Gaurav K, Kumar R, Kumar A and Bhondekar A P 2018 Exploring robot behavior in search of a chemical source. In: Proceedings of the International Conference on Intelligent Autonomous Systems, Singapore, March 1–3, pp. 142–145
Gaurav K, Dayal R and Kumar A 2019 Scope of improvement in algorithm for odor source localization in an indoor dynamic environment: a preliminary study. Int. J. Electron. Electr. Eng. 7(2): 27–32
Cabrita G and Marques L 2013 Divergence-based odor source declaration. In: Proceedings of the Control Conference (ASCC), 2013 9th Asian, pp. 1–6
Li W 2006 Abstraction of odor source declaration algorithm from moth-inspired plume tracing strategies. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics,ROBIO’06., pp. 1024–1029
Vergassola M, Villermaux E and Shraiman B I 2007 ‘Infotaxis’ as a strategy for searching without gradients. Nature 445(7126): 406
Ishida H, Kagawa Y, Nakamoto T and Moriizumi T 1996 Odor-source localization in the clean room by an autonomous mobile sensing system. Sens. Actuat. B Chem. 33(1): 115–121
Ishida H, Tanaka H, Taniguchi H and Moriizumi T 2006 Mobile robot navigation using vision and olfaction to search for a gas/odor source. Auton. Robots 20(3): 231–238
Awadalla M, Lu T-F, Tian Z F, Dally B and Liu Z 2013 3D framework combining CFD and MATLAB techniques for plume source localization research. Build. Environ. 70: 10–19
Zhang Y, Zhang J, Hao G and Zhang W 2015 Localizing odor source with multi-robot based on hybrid particle swarm optimization. In: Proceedings of the Natural Computation (ICNC), 2015 11th International Conference on, pp. 902–906
Wang J, Zhang R, Yan Y, Dong X and Li J M 2017 Locating hazardous gas leaks in the atmosphere via modified genetic, MCMC and particle swarm optimization algorithms. Atmos. Environ. 157: 27–37
Marques L, Nunes U and de Almeida A T 2006 Particle swarm-based olfactory guided search. Auton. Robots 20(3): 277–287
Zou Y and Luo D 2008 A modified ant colony algorithm used for multi-robot odor source localization. In: Proceedings of the International Conference on Intelligent Computing, pp. 502–509
Jatmiko W, Nugraha A, Effendi R, Pambuko W, Mardian R, Sekiyama K, et al 2009 Localizing multiple odor sources in a dynamic environment based on modified niche particle swarm optimization with flow of wind. WSEAS Transactions on Systems 8(11): 1187–1196
Marjovi A and Marques L 2011 Multi-robot olfactory search in structured environments. Robot. Auton. Syst. 59(11): 867–881
Zhang J, Gong D and Zhang Y 2014 A niching PSO-based multi-robot cooperation method for localizing odor sources. Neurocomputing 123: 308–317
Jain U, Tiwari R and Godfrey W W 2019 Multiple odor source localization using diverse-PSO and group-based strategies in an unknown environment. J. Comput. Sci. 34: 33–47
Feng Q, Zhang C, Lu J, Cai H, Chen Z, Yang Y, et al 2019 Source localization in dynamic indoor environments with natural ventilation: an experimental study of a particle swarm optimization-based multi-robot olfaction method. Build. Environ. 161:106228
Yang X, Yuan J, Yuan J and Mao H 2007 A modified particle swarm optimizer with dynamic adaptation. Appl. Math. Comput. 189(2): 1205–1213
Chen X-x and Huang J 2019 Odor source localization algorithms on mobile robots: a review and future outlook. Robot. Auton. Syst. 112: 123–136
Zheng Y-L, Ma L-H, Zhang L-Y and Qian J-X 2003 On the convergence analysis and parameter selection in particle swarm optimization. In: Proceedings of the International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), pp. 1802–1807
Eberhart R C and Shi Y 2001 Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), pp. 94–100
Eberhart R C and Shi Y 2000 Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), pp. 84–88
Gong D-W, Qi C-L, Zhang Y and Li M 2011 Modified particle swarm optimization for odor source localization of multi-robot. In: Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 130–136
Jatmiko W, Sekiyama K and Fukuda T 2006 Modified particle swarm robotic for odor source localization in dynamic environment. Int. J. Intell. Control Syst. Spec. Issue Swarm Robot. 11(3): 176–184
Yan Y, Zhang R, Wang J and Li J 2018 Modified PSO algorithms with “Request and Reset” for leak source localization using multiple robots. Neurocomputing 292: 82–90
Li F, Meng Q-H, Bai S, Li J-G and Popescu D 2008 Probability-PSO algorithm for multi-robot based odor source localization in ventilated indoor environments. In: Proceedings of the International Conference on Intelligent Robotics and Applications, pp. 1206–1215
Meng Q-H, Yang W-X, Wang Y and Zeng M 2011 Collective odor source estimation and search in time-variant airflow environments using mobile robots. Sensors 11(11): 10415–10443
Jain U, Godfrey W W and Tiwari R 2017 A hybridization of gravitational search algorithm and particle swarm optimization for odor source localization. Int. J. Robot. Appl. Technol. (IJRAT) 5(1): 20–33
Jain U, Tiwari R and Godfrey W W 2018 Odor source localization by concatenating particle swarm optimization and Grey Wolf optimizer. In: Advanced Computational and Communication Paradigms, Springer, pp. 145–153
Rao R V, Savsani V J and Vakharia D 2011 Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3): 303–315
Singh R, Chaudhary H and Singh A K 2017 A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path. Sādhanā 42(11): 1851–1870
Matthes J, Groll L and Keller H B 2005 Source localization by spatially distributed electronic noses for advection and diffusion. IEEE Trans. Signal Process. 53(5): 1711–1719
Cao M L, Meng Q H, Wu Y X, Zeng M and Li W 2013 Consensus based distributed concentration-weighted summation algorithm for gas-leakage source localization using a wireless sensor network. In: Proceedings of the Control Conference (CCC), 2013 32nd Chinese, pp. 7398–7403
Shu L, Mukherjee M, Xu X, Wang K and Wu X 2016 A survey on gas leakage source detection and boundary tracking with wireless sensor networks. IEEE Access 4: 1700–1715
Stockie J M 2011 The mathematics of atmospheric dispersion modeling. Siam Rev. 53(2): 349–372
Singh R, Chaudhary H and Singh A K 2017 Defect-free optimal synthesis of crank-rocker linkage using nature-inspired optimization algorithms. Mech. Mach. Theory 116: 105–122
Eberhart R and Kennedy J 1995 Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948
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Gaurav, K., Kumar, A. & Singh, R. Single and multiple odor source localization using hybrid nature-inspired algorithm. Sādhanā 45, 83 (2020). https://doi.org/10.1007/s12046-020-1318-3
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DOI: https://doi.org/10.1007/s12046-020-1318-3