From Robot Self-Localization to Global-Localization: An RSSI Based Approach (2112.10578v2)
Abstract: Localization is a crucial task for autonomous mobile robots in order to successfully move to goal locations in their environment. Usually, this is done in a robot-centric manner, where the robot maintains a map with its body in the center. In swarm robotics applications, where a group of robots needs to coordinate in order to achieve their common goals, robot-centric localization will not suffice as each member of the swarm has its own frame of reference. One way to deal with this problem is to create, maintain and share a common map (global coordinate system), among the members of the swarm. This paper presents an approach to global localization for a group of robots in unknown, GPS and landmark free environments that extends the localization scheme of the LadyBug algorithm. The main idea relies on members of the swarm staying still and acting as beacons, emitting electromagnetic signals. These stationary robots form a global frame of reference and the rest of the group localize themselves in it using the Received Signal Strength Indicator (RSSI). The proposed method is evaluated, and the results obtained from the experiments are promising.
- In: VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, pp. 1–5, 10.1109/VETECS.2009.5073315.
- IEEE Transactions on Intelligent Vehicles 2(3), pp. 194–220, 10.1109/TIV.2017.2749181.
- I.J. Cox (1991): Blanche-an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation 7(2), pp. 193–204, 10.1109/70.75902.
- Alan Oliveira de Sa, Nadia Nedjah & Luiza de Macedo Mourelle (2016): Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms. Neurocomputing 172, pp. 322–336, 10.1016/j.neucom.2015.03.099. Available at https://www.sciencedirect.com/science/article/pii/S0925231215010498.
- Qian Dong & Waltenegus Dargie (2012): Evaluation of the reliability of RSSI for indoor localization. In: 2012 International Conference on Wireless Communications in Underground and Confined Areas, pp. 1–6, 10.1109/ICWCUCA.2012.6402492.
- Salvatore D’Avella, Matteo Unetti & Paolo Tripicchio (2022): RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking. IEEE Access 10, pp. 22150–22160, 10.1109/ACCESS.2022.3152199.
- Giovanni Fusco & James M. Coughlan (2018): Indoor Localization Using Computer Vision and Visual-Inertial Odometry. In Klaus Miesenberger & Georgios Kouroupetroglou, editors: Computers Helping People with Special Needs, Springer International Publishing, Cham, pp. 86–93, 10.1007/978-3-319-94274-2_13.
- Sensors 20(6), 10.3390/s20061578. Available at https://www.mdpi.com/1424-8220/20/6/1578.
- Koen Langendoen & Niels Reijers (2003): Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks 43(4), pp. 499–518, 10.1016/S1389-1286(03)00356-6. Available at https://www.sciencedirect.com/science/article/pii/S1389128603003566. Wireless Sensor Networks.
- Athanasios Lentzas & Dimitris Vrakas (2020): LadyBug. An Intensity based Localization Bug Algorithm. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1, pp. 682–689, 10.1109/ETFA46521.2020.9212115.
- Junjie Liu (2014): Survey of Wireless Based Indoor LocalizationTechnologies. Department of Science and Engineering Washington University.
- O. Michel (2004): Webots: Professional Mobile Robot Simulation. Journal of Advanced Robotics Systems 1(1), pp. 39–42. Available at http://www.ars-journal.com/International-Journal-of-Advanced-Robotic-Systems/Volume-1/39-42.pdf.
- Nadia Nedjah & Luneque Silva Junior (2019): Review of methodologies and tasks in swarm robotics towards standardization. Swarm and Evolutionary Computation 50, p. 100565, 10.1016/j.swevo.2019.100565. Available at https://www.sciencedirect.com/science/article/pii/S2210650217308398.
- Wireless Personal Communications, 10.1007/s11277-021-08209-5.
- Enrico Petritoli, Fabio Leccese & Mariagrazia Leccisi (2019): Inertial Navigation Systems for UAV: Uncertainty and Error Measurements. In: 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 1–5, 10.1109/MetroAeroSpace.2019.8869618.
- Joseph A. Rothermich, M. İhsan Ecemiş & Paolo Gaudiano (2005): Distributed Localization and Mapping with a Robotic Swarm. In Erol Şahin & William M. Spears, editors: Swarm Robotics, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 58–69, 10.1007/978-3-540-30552-1_6.
- Stergios I. Roumeliotis & George A. Bekey (2000): Distributed Multi-Robot Localization, pp. 179–188. Springer Japan, Tokyo, 10.1007/978-4-431-67919-6_17.
- Michael Rubenstein, Alejandro Cornejo & Radhika Nagpal (2014): Programmable self-assembly in a thousand-robot swarm. Science 345(6198), pp. 795–799, 10.1126/science.1254295.
- Jirapat Sangthong, Jutamas Thongkam & Sathapom Promwong (2020): Indoor Wireless Sensor Network Localization Using RSSI Based Weighting Algorithm Method. In: 2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 1–4, 10.1109/ICEAST50382.2020.9165300.
- Andreas Savvides, Heemin Park & Mani B. Srivastava (2002): The Bits and Flops of the N-Hop Multilateration Primitive for Node Localization Problems. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, WSNA ’02, Association for Computing Machinery, New York, NY, USA, p. 112–121, 10.1145/570738.570755.
- M. Stella, M. Russo & D. Begušić (2014): Fingerprinting based localization in heterogeneous wireless networks. Expert Systems with Applications 41(15), pp. 6738–6747, 10.1016/j.eswa.2014.05.016. Available at https://www.sciencedirect.com/science/article/pii/S0957417414002966.
- Kamilah Taylor & Steven M. LaValle (2009): I-Bug: An intensity-based bug algorithm. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3981–3986, 10.1109/ROBOT.2009.5152728.
- Expert Systems with Applications 37(1), pp. 894–898, 10.1016/j.eswa.2009.05.062. Available at https://www.sciencedirect.com/science/article/pii/S095741740900503X.
- Xiaoguang Wan & Xingqun Zhan (2011): The Research of Indoor Navigation System using Pseudolites. Procedia Engineering 15, pp. 1446–1450, 10.1016/j.proeng.2011.08.268. Available at https://www.sciencedirect.com/science/article/pii/S1877705811017693. CEIS 2011.
- Webots: http://www.cyberbotics.com. Available at http://www.cyberbotics.com. Commercial Mobile Robot Simulation Software.
- Sensors 15(5), pp. 10074–10087, 10.3390/s150510074. Available at https://www.mdpi.com/1424-8220/15/5/10074.
- Zheng Yang, Zimu Zhou & Yunhao Liu (2013): From RSSI to CSI: Indoor Localization via Channel Response. ACM Comput. Surv. 46(2), 10.1145/2543581.2543592.