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Multi-Agent Systems Research Group
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Systems and Control Group
Department of Information Engineering and Mathematics
University of Siena





Summary

1  People
2  Research
    2.1  Simultaneous Localization and Map Building (SLAM)
    2.2  Collective Motion of Multi-Agent Systems
    2.3  Opinion Dynamics and Consensus
    2.4  Pursuit-Evasion Games
3  Videos
    3.1  Single-robot SLAM using linear features
    3.2  Multi-robot SLAM using M-Space feature representation
    3.3  Circular motion of nonholonomic vehicles
    3.4  Remote lab for multi-agent systems
4  Publications





1  People

Faculty

Ph.D. Students

Former collaborators



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2  Research

2.1  Simultaneous Localization and Map Building (SLAM)

State estimation techniques are developed for the simultaneous localization and map building (SLAM) problem. Both deterministic (set-membership) and probabilistic descriptions of the uncertainty are considered. Different environment representations are adopted (pointwise landmarks and linear features). The developed algorithms are suitably extended to the multi-robot scenario.
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Set-membership techniques

Probabilistic techniques



2.2  Collective Motion of Multi-Agent Systems

This research line deals with the coordination of multi-agent systems. A decentralized control law for the collective circular motion is developed and its stability properties are analyzed. Practical issues like collision avoidance and the limited field of view of the sensors are explicitly taken into account. The proposed control law is validated on real robots. Additionally, a remote lab for experimenting with small LEGO vehicles is available. images/circular.jpg


2.3  Opinion Dynamics and Consensus

The asymptotic behavior of threshold models used to describe the evolution of opinion dynamics and the formation of collective actions in social networks is studied. The proposed model introduces a parameter accounting for the level of self-confidence of the agents, which affects the dynamic evolution of the threshold and in turn the way the agents make their decision. The impact that the network topology has on the asymptotic behavior of the system is studied both analytically and via numerical simulation.
A related research line concerns the performance of consensus protocols in the presence of bounded measurement errors. Both static and dynamic weights are considered. Bounds and the maximum deviation from consensus are derived in terms of the structure of the weight matrix and the maximum magnitude of the measurement errors.
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2.4  Pursuit-Evasion Games

In the framework of Pursuit-Evasion Games (PEGs), the so-called Lion and Man problem has been studied. In this game, a lion (pursuer) and a man (evader) move alternately in the positive quadrant of the plane, traveling a distance of at most one unit at each move. Capture occurs when the players' locations coincide. Novel strategies outperforming existing ones have been proposed as well as associated bounds on the capture time. Such results have also been extended to polynomial environments.
Pursuit-Evasion Games involving one evader and multiple pursuers have been investigated as well. In particular, games involving three pursuers and one evader has been analyzed, deriving results on how pursuer cooperation may improve capture time with respect to the decentralized case.
  • Lion and Man problem [28,29,30,31]
  • Multi-pursuer single-evader game


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3  Videos

3.1  Single-robot SLAM using linear features

A Pioneer 3AT mobile robot performing SLAM in our former lab, under Bernardino Fungai's "Last Supper" fresco (15th century) [10,11]. The non orthogonal walls in the final map are actually like this old building is made!

This video shows the effect of loop closure in a simulated environment. Notice how the map is registered when the robot recognizes already visited places.


3.2  Multi-robot SLAM using M-Space feature representation

This video shows the a run of the multi-robot SLAM agorithm, using linear features and M-Space representation [12,13]. Two Pioneer 3AT robots explore the second floor of our Department (about 3000 m2), starting from different unknown locations. When they meet, relative measurements are taken and the local maps are merged together.

3.3  Circular motion of nonholonomic vehicles

The first video shows a simulation of four unicycles tracking a moving target while rotating around it, resulting from the application of the control law proposed in [14,15]. The second video shows an experiment of circular motion about a stationary target performed with a team of LEGO Mindstorms robots [16,18,19].


3.4  Remote lab for multi-agent systems

The first video shows how to remotely perform a multi-robot experiment through the Automatic Control Telelab, developed at the University of Siena [20,21,22,23,24]. The second video shows the actual robots moving during a motion coordination experiment and then autonomously returning to the recharge station.



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4  Publications

[1]
A. Garulli and A. Vicino. Set membership localization of mobile robots via angle measurements. IEEE Transactions on Robotics and Automation, 17(4):450-463, August 2001.
[2]
M. Di Marco, A. Garulli, S. Lacroix, and A. Vicino. Set membership localization and mapping for autonomous navigation. International Journal of Robust and Nonlinear Control, 11(7):709-734, 2001.
[3]
M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. A set theoretic approach to dynamic robot localization and mapping. Autonomous Robots, 16(1):23-47, 2004.
[4]
M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. Set membership pose estimation of mobile robots based on angle measurements. In Proceedings of the 40th IEEE Conference on Decision and Control, volume 4, pages 3734-3739, Orlando (USA), December 4-7 2001.
[5]
M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. Dynamic robot localization and mapping using uncertainty sets. In Proceedings of the 15th IFAC World Congress, Barcelona (Spain), July 21-26 2002.
[6]
N. Ceccarelli, M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. Set membership localization and map building for mobile robots. In L. Menini, L. Zaccarian, and C.T. Abdallah, editors, Current trends in nonlinear systems and control, pages 289-308. Birkäuser, 2006.
[7]
M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. Simultaneous localization and map building for a team of cooperating robots: A set membership approach. IEEE Transactions on Robotics and Automation, 19(2):238-249, 2003.
[8]
N. Ceccarelli, M. Di Marco, A. Garulli, A. Giannitrapani, and A. Vicino. Path planning with uncertainty: A set membership approach. International Journal of Adaptive Control and Signal Processing, 25(3):273-287, 2011.
[9]
N. Ceccarelli, M. Di Marco, A. Garulli, and A. Giannitrapani. A set theoretic approach to path planning for mobile robots. In Proceedings of the 43rd IEEE Conference on Decision and Control, volume 1, pages 147-152, Atlantis (Bahamas), December 14-17 2004.
[10]
A. Garulli, A. Giannitrapani, A. Rossi, and A. Vicino. Simultaneous localization and map building using linear features. In Proceedings of the 2nd European Conference on Mobile Robots, pages 44-49, Ancona (Italy), September 7-10 2005.
[11]
A. Garulli, A. Giannitrapani, A. Rossi, and A. Vicino. Mobile robot SLAM for line-based environment representation. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, volume 2005, pages 2041-2046, Seville (Spain), December 12-15 2005.
[12]
D. Benedettelli, A. Garulli, and A. Giannitrapani. Cooperative SLAM using M-Space representation of linear features. Robotics and Autonomous Systems, 60(10):1267-1278, 2012.
[13]
D. Benedettelli, A. Garulli, and A. Giannitrapani. Multi-robot SLAM using M-Space feature representation. In Proceedings of the 49th IEEE Conference on Decision and Control, pages 3826-3831, Atlanta (USA), December 15-17 2010.
[14]
N. Ceccarelli, M. Di Marco, A. Garulli, and A. Giannitrapani. Collective circular motion of multi-vehicle systems. Automatica, 44(12):3025-3035, 2008.
[15]
N. Ceccarelli, M. Di Marco, A. Garulli, and A. Giannitrapani. Collective circular motion of multi-vehicle systems with sensory limitations. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, volume 2005, pages 740-745, Seville (Spain), December 12-15 2005.
[16]
D. Benedettelli, N. Ceccarelli, A. Garulli, and A. Giannitrapani. Experimental validation of collective circular motion for nonholonomic multi-vehicle systems. Robotics and Autonomous Systems, 58(8):1028-1036, 2010.
[17]
N. Ceccarelli, M. Di Marco, A. Garulli, and A. Giannitrapani. Experimental analysis of collective circular motion for multi-vehicle systems. In Proceedings of the 8th International IFAC Symposium on Robot Control, volume 8, Bologna (Italy), Septmeber 6-8 2006.
[18]
D. Benedettelli, N. Ceccarelli, A. Garulli, and A. Giannitrapani. Experimental validation of a decentralized control law for multi-vehicle collective motion. In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4170-4175, San Diego (USA), October 29 - November 2 2007.
[19]
D. Benedettelli, M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. A LEGO Mindstorms experimental setup for multi-agent systems. In Proceedings of the 3rd IEEE Multi-Conference on Systems and Control, pages 1230-1235, St. Petersburg (Russia), July 8-10 2009.
[20]
M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. A remote lab for experiments with a team of mobile robots. Sensors, 14(9):16486-16507, 2014.
[21]
M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. A Matlab-based remote lab for multi-robot experiments. In Proceedings of the 8th IFAC Symposium on Advances in Control Education, volume 8, pages 162-167, Kumamoto (Japan), October 21-23 2009.
[22]
M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. A LEGO Mindstorms multi-robot setup in the Automatic Control Telelab. In Proceedings of the 18th IFAC World Congress, volume 18, pages 9812-9817, Milan (Italy), August 28 - September 2 2011.
[23]
M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. A remote lab for multi-robot experiments with virtual obstacles. In Proceedings of the 9th IFAC Symposium on Advances in Control Education, volume 9, pages 354-359, Nizhny Novgorod (Russia), June 19-21 2012.
[24]
M. Casini, A. Garulli, A. Giannitrapani, and A. Vicino. Remote pursuer-evader experiments with mobile robots in the automatic control telelab. In Proceedings of the 10th IFAC Symposium on Advances in Control Education, volume 10, pages 66-71, Sheffield (U.K.), August 28-30 2013.
[25]
A. Garulli, A. Giannitrapani, and M. Valentini. Analysis of threshold models for collective actions in social networks. In Proceedings of the 14th European Control Conference, pages 211-216, Linz (AT), July 15-17 2015.
[26]
A. Garulli and A. Giannitrapani. Analysis of consensus protocols with bounded measurement errors. Systems and Control Letters, 60(1):44-52, 2011.
[27]
A. Garulli and A. Giannitrapani. A set-membership approach to consensus problems with bounded measurement errors. In Proceedings of the 47th IEEE Conference on Decision and Control, pages 2276-2281, Cancun (Mexico), December 9-11 2008.
[28]
M. Casini and A. Garulli. A novel family of pursuit strategies for the lion and man problem. In 56th IEEE Conference on Decision and Control (CDC), pages 6436-6441, Melbourne, December 2017.
[29]
M. Casini and A. Garulli. An improved lion strategy for the lion and man problem. IEEE Control Systems Letters (L-CSS), 1(1):38-43, 2017.
[30]
M. Casini and A. Garulli. A new class of pursuer strategies for the discrete-time lion and man problem. Automatica, 100:162-170, 2019.
[31]
M. Casini, M. Criscuoli, and A. Garulli. A new class of pursuer strategies for the discrete-time lion and man problem. Systems & Control Letters, 125:22-28, 2019.



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