Abstract
Source seeking problem has been faced in many fields, especially in search and rescue applications such as first-response rescue, gas leak search, etc. We proposed a virtual structure based spatial-temporal method to realize cooperative source seeking using multi-agents. Spatially, a circular formation is considered to gather collaborative information and estimate the gradient direction of the formation center. In terms of temporal information, we make use of the formation positions in time sequence to construct a virtual structure sequence. Then, we fuse the sequential gradient as a whole. A control strategy with minimum movement cost is proposed. This strategy rotates the target formation by a certain angle to make the robot team achieve the minimum moving distance value when the circular team moves to the next position. Experimental results show that, compared with state-of-the-art, the proposed method can quickly find the source in as few distances as possible, so that the formation can minimize the movement distance during the moving process, and increase the efficiency of source seeking. Numerical simulations confirm the efficiency of the scheme put forth. Compared with state-of-the-art source seeking methods, the iterative steps of our proposed method is reduced by 20%, indicating that the method can find the signal source with higher efficiency and lower energy consumption, as well as better robustness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waleed, D., et al.: An in-pipe leak detection robot with a neural-network-based leak verification system. IEEE Sens. J. 19(3), 1153–1165 (2019)
Berjaoui, S., Alkhatib, R., Elshiekh, A., Morad, M., Diab, M.O.: Free flowing robot for automatic pipeline leak detection using piezoelectric film sensors. In: International Mediterranean Gas and Oil Conference (MedGO). Mechref 2015, pp. 1–3 (2015)
Watiasih, R., Rivai, M., Penangsang, O., Budiman, F., Tukadi, Izza, Y.: Online gas mapping in outdoor environment using solar-powered mobile robot. In: 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, pp. 245–250 (2018)
Che, H., Shi, C., Xu, X., Li, J., Wu, B.: Research on improved ACO algorithm-based multi-robot odor source localization. In: 2018 2nd International Conference on Robotics and Automation Sciences (ICRAS), Wuhan, pp. 1–5 (2018)
Cao, X., Jin, Z., Wang, C., Dong, M.: Kinematics simulation of environmental parameter monitor robot used in coalmine underground. In: 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi’an, pp. 576–581 (2016)
Shin, H., Kim, C., Seo, Y., Eom, H., Choi, Y., Kim, M.: Aerial working environment monitoring robot in high radiation area. In: 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), Seoul, pp. 474–478 (2014)
Shin, D., Na, S.Y., Kim, J.Y., Baek, S.: Fish robots for water pollution monitoring using ubiquitous sensor networks with sonar localization. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007), Gyeongju, pp. 1298–1303 (2007)
Kanwar, M., Agilandeeswari, L.: IOT based fire fighting robot. In: 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 718–723 (2018)
Chen, X., Zhang, H., Lu, H., Xiao, J., Qiu, Q., Li, Y.: Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue. In: IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), Shanghai 2017, pp. 41–47 (2017)
Denker, A., İşeri, M.C.: Design and implementation of a semi-autonomous mobile search and rescue robot: SALVOR. In: International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, pp. 1–6 (2017)
Consi, T., Atema, J., Goudey, C., Cho, J., Chryssostomidis, C.: AUV guidance with chemical signals. In: Proceedings of the 1994 Symposium on Autonomous Underwater Vehicle Technology, AUV 1994, pp. 450–455. IEEE (1994)
Kuwana, Y., Nagasawa, S., Shimoyama, I., Kanzaki, R.: Synthesis of the pheromone-oriented behaviour of silkworm moths by a mobile robot with moth antennae as pheromone sensors1. Biosens. Bioelectron. 14(2), 195–202 (1999)
Russell, R.A., Bab-Hadiashar, A., Shepherd, R.L., Wallace, G.G.: A comparison of reactive robot chemotaxis algorithms. Robot. Auton. Syst. 45(2), 83–97 (2003)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)
Karaboga, D.: An idea based on honeybee swarm for numerical optimization. Technical Report TR06. Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Wu, H.S., Zhang, F., Wu, L.: New swarm intelligence algorithm-wolf pack algorithm. Syst. Eng. Electron. 35(11), 2430–2438 (2013)
Neto, M.T.R.S., Mollinetti, M.A.F., Pereira, R.L.: Evolutionary artificial bee colony for neural networks training. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, pp. 44–49 (2017)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, Perth, WA, Australia, vol. 4, pp. 1942–1948 (1995)
Jatmiko, W., Sekiyama, K., Fukuda, T.: Apso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement. IEEE Comput. Intell. Mag. 2(2), 37–51 (2007)
Jatmiko, W., et al.: Robots implementation for odor source localization using PSO algorithm. WSEAS Trans. Circuits Syst. 10(4), 115–125 (2011)
Li, F., Meng, Q.-H., Bai, S., Li, J.-G., Popescu, D.: Probability-PSO algorithm for multi-robot based odor source localization in ventilated indoor environments. In: Xiong, C., Huang, Y., Xiong, Y., Liu, H. (eds.) ICIRA 2008. LNCS (LNAI), vol. 5314, pp. 1206–1215. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88513-9_128
Wu, W., Zhang, F.: A speeding-up and slowing-down strategy for distributed source seeking with robustness analysis. IEEE Trans. Control Netw. Syst. 3(3), 231–240 (2016)
Al-Abri, S., Wu, W., Zhang, F.: A gradient-free three-dimensional source seeking strategy with robustness analysis. IEEE Trans. Autom. Control 64(8), 3439–3446 (2019)
Liu, S.-J., Krstic, M.: Stochastic source seeking for nonholonomic unicycle. Automatica 46(9), 1443–1453 (2010)
Cochran, J., Krstic, M.: Source seeking with a nonholonomic unicycle without position measurements and with tuning of angular velocity part I: stability analysis. In: 2007 46th IEEE Conference on Decision and Control, New Orleans, LA, pp. 6009–6016 (2007)
Atanasov, N., Le Ny, J., Michael, N., Pappas, G.J.: Stochastic source seeking in complex environments. In: 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, pp. 3013–3018 (2012)
Ogren, P., Fiorelli, E., Leonard, N.E.: Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment. IEEE Trans. Autom. Control 49(8), 1292–1302 (2004)
Zhu, S., Wang, D., Low, C.B.: Cooperative control of multiple UAVs for moving source seeking. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, pp. 193–202 (2013)
Li, S., Kong, R., Guo, Y.: Cooperative distributed source seeking by multiple robots: algorithms and experiments. IEEE/ASME Trans. Mechatron. 19(6), 1810–1820 (2014)
Fabbiano, R., Garin, F., Canudas-de-Wit, C.: Distributed source seeking without global position information. IEEE Trans. Control Netw. Syst. 5(1), 228–238 (2018)
Briñón-Arranz, L., Renzaglia, A., Schenato, L.: Multi-robot symmetric formations for gradient and hessian estimation with application to source seeking. IEEE Trans. Robot. 35, 782–789 (2019)
Briñón-Arranz, L., Seuret, A., Pascoal, A.: Circular formation control for cooperative target tracking with limited information. J. Franklin Inst. 356, 1771–1788 (2019). https://doi.org/10.1016/j.jfranklin.2018.12.011
Spall, J.: Intro to Stochastic Search and Optimization. Wiley, Hoboken (2003)
Acknowledgments
This work is supported in part by National Postdoctoral Program for Innovative Talents under Grant BX20190033, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110325, in part by Project funded by China Postdoctoral Science Foundation under Grant 2020M670135, in part by Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing under Grant 2020BH001, and in part by the Fundamental Research Funds for the Central Universities under Grant 06500127 and FRF-GF-19-018B.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, C., Chen, Y., Duan, S., Wu, H., Qi, Y. (2021). Cooperative Source Seeking in Scalar Field: A Virtual Structure-Based Spatial-Temporal Method. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-67540-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67539-4
Online ISBN: 978-3-030-67540-0
eBook Packages: Computer ScienceComputer Science (R0)