Formation Control of Multi-Agent Systems with Location Uncertainty
In this chapter the impact of realistic communication channels and uncertain location information on formation control of multi-agent systems aiming to achieve a common task is highlighted. First, the work is motivated by elucidating the need to incorporate realistic communication models as well as the need to model the agents’ location uncertainty. Second, it is discussed how control can be utilised to reduce the agents positioning error in cooperative systems to achieve a higher level goal, such as steering a group of agents towards a destination. Third, the impact of location uncertainty on channel gain prediction is addressed for formation control. Finally, conclusions and an outlook on future directions for controlled multi-agent systems are provided.
KeywordsMulti-agent systems Gaussian processes Channel prediction Formation control Swarm navigation Cramér-Rao bound Location uncertainty Spatial correlation
This work was financially supported by EU FP7 Marie Curie Initial Training Network MULTI-POS (Multi-technology Positioning Professionals) under grant nr. 316528. This work was also partially supported by the German project VaMEx-CoSMiC, which is supported by the Federal Ministry for Economic Affairs and Energy on the basis of a decision by the German Bundestag, grant 50NA1521 administered by DLR Space Administration, the EU project HIGHTS MG-3.5a-2014-636537, the European Research Council under Grant No. 258418 (COOPNET); and the DLR project Dependable Navigation.
- 2.A. Böttcher, P. Vary, C. Schneider, R.S. Thomä, De-correlation distance of the large scale parameters in an urban macro cell scenario, in 6th European Conference on Antennas and Propagation (EUCAP) (2012), pp. 1417–1421Google Scholar
- 4.D. Cohen, D.L. Jones, S. Narayanan, Expected-utility-based Sensor Selection for State Estimation, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, New York, 2012), pp. 2685–2688Google Scholar
- 8.J. Fink, Communication for Teams of Networked Robots. Ph.D. thesis, Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, August 2011Google Scholar
- 9.M. Fröhle, A.A. Zaidi, E. Ström, H. Wymeersch, Multi-step sensor selection with position uncertainty constraints, in IEEE Globecom Workshops (2014), pp. 1439–1444. doi: 10.1109/GLOCOMW.2014.7063636.
- 12.M. Grant, S. Boyd, CVX: Matlab Software for Disciplined Convex Programming, March 2014. http://cvxr.com/cvx
- 13.M. Gudmundson, Correlation model for shadow fading in mobile radio systems. Electron. Lett. 27 (23), 2145–2146 (1991). ISSN: 0013-5194. doi: 10.1049/el:19911328
- 14.M.F. Huber, On multi-step sensor scheduling via convex optimization, in 2nd International Workshop on Cognitive Information Processing (CIP), June 2010, pp. 376–381Google Scholar
- 15.N. Jalden, Analysis and Modelling of Joint Channel Properties from Multi-site, Multi-Antenna Radio Measurements. Ph.D. thesis, KTH, Signal Processing, 2010, pp. xviii, 224Google Scholar
- 18.Y. Kim, G. Zhu, J. Hu, Optimizing formation rigidity under connectivity constraints, in IEEE Conference on Decision and Control (CDC), 2010, pp. 6590–6595Google Scholar
- 19.G.J.M. Kruijff et al., Experience in system design for human–robot teaming in urban search and rescue, in Field and Service Robotics (Springer, Berlin, Heidelberg, 2014), pp. 111–125Google Scholar
- 24.R. Olfati-Saber, Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51 (3), 401–420 (2006). ISSN: 0018-9286. doi: 10.1109/TAC.2005.864190
- 29.Y. Shen, H. Wymeersch, M.Z. Win, Fundamental limits of wideband localization. Part II: cooperative networks. IEEE Trans. Inform. Theory 56 (10), 4981–5000 (2010). ISSN: 0018-9448. doi: 10.1109/TIT.2010.2059720
- 32.S. Thrun, Y. Liu, Multi-robot SLAM with sparse extended information filers, in Robotics Research (Springer, Berlin, 2005), pp. 254–266Google Scholar
- 34.Z. Wang, E.K. Tameh, A.R. Nix, Joint shadowing process in urban peer-to-peer radio channels. IEEE Trans. Veh. Technol. 57 (1), 52–64 (2008). ISSN: 0018-9545. doi: 10.1109/TVT.2007.904513
- 36.H. Wymeersch, J. Lien, M.Z. Win, Cooperative localization in wireless networks. Proc. IEEE 97 (2), 427–450 (2009). ISSN: 0018-9219. doi: 10.1109/JPROC.2008.2008853
- 37.Y. Yan, Y. Mostofi, Impact of localization errors on wireless channel prediction in mobile robotic networks, in IEEE Globecom, Workshop on Wireless Networking for Unmanned Autonomous Vehicles, December 2013Google Scholar
- 39.S. Zhang, R. Raulefs, Multi-agent flocking with noisy anchor-free localization, in 11th International Symposium on Wireless Communications Systems (ISWCS) (2014), pp. 927–933Google Scholar
- 40.S. Zhang et al., System-level performance analysis for Bayesian cooperative positioning: from global to local, in International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2013, pp. 1–10Google Scholar
- 41.S. Zhang, M. Fröhle, H. Wymeersch, A. Dammann, R. Raulefs, Location-aware formation control in swarm navigation, in 2015 IEEE Globecom Workshops (2015), pp. 1–6. doi: 10.1109/GLOCOMW.2015.7414165
- 42.S. Zhang, R. Raulefs, A. Dammann, Localization-driven formation control for swarm return-to-base application, in European Signal Processing, 2016 IEEE/EURASIP Conference on (EUSIPCO), August 2016Google Scholar