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UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

De nardi, Renzo and Holland, Owen (2006) UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters. [Conference Paper]

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Abstract

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking.

Item Type:Conference Paper
Keywords:Miniature helicopter, Swarm intelligence, Neural Network, Evolution
Subjects:Computer Science > Robotics
ID Code:5571
Deposited By:De Nardi, Mr Renzo
Deposited On:28 May 2007
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Reynolds, C.: Flocks, herds, and schools: A distributed behavioral model. In: Proceedings of the Conference on Computer Graphics (SIGGRAPH). Volume 21:4.(1987) 25–34

Mataric, M.: Interaction and intelligent behavior. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (1995)

Kelly, I., Keating, D.: Flocking by the fusion of sonar and active infrared sensors on physical autonomous mobile robots. In: Proceedings of The Third Int. Conf. on Mechatronics and Machine Vision in Practice. Volume 1.(1996) 1–4

Welsby, J., Melhuish, C.: Autonomous minimalist following in three dimensions: A study with small-scale dirigibles. In: Proceedings of Towards Intelligent Mobile Robots Manchster. (2001)

Crowther, B., Riviere, X.: Flocking of autonomous unmanned air vehicles. In: Proceeding of the 17th UAV System conference, Bristol UK. (2002)

Calise, A., Preston, D.: Swarming/flocking and collision avoidance for mass airdrop of autonomous guided parafoils. In: AIAA Guidance, Navigation, and Control Conference and Exhibit. (2005)

Jadbabaie, A., Lin, J., Morse, A.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Automatic Control 48(6) (2003) 998–1001

Tanner, H., Jadbabaie, A., Pappas, G.: Stable flocking of mobile agents. part I: Static topology. In: Proceedings of the 42nd IEEE Conference on Decision and Control. (2003) 2010–2015

Tanner, H., Jadbabaie, A., Pappas, G.: Stable flocking of mobile agents. part II: Dynamic topology. In: Proceedings of the 42nd IEEE Conference on Decision and Control. (2003) 2016–2021

Gazi, V., Fidan, B.: Review of control and coordination of multi-agent dynamic systems: models and approaches. In Sahin, E., Spears, W., Winfield, A., eds.: Swarm Robotics Workshop (SAB06). Lecture Notes in Computer Science (2006)

Olfati-Saber, R.: Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transaction on Automatic Control 51(3) (March 2006)

Samiloglu, a., Gazi, V., Koku, B.: An empirical study on the motion of selfpropelled particles with turn angle restrictions. In Sahin, E., Spears, W., Winfield,

A., eds.: Swarm Robotics Workshop (SAB06). Lecture Notes in Computer Science (2006)

Holland, O.E., Woods, J., De Nardi, R., Clark, A.: Beyond swarm intelligence: The UltraSwarm. In: Proceedings of the IEEE Swarm Intelligence Symposium (SIS2005), IEEE (2005)

Jun, M., Roumeliotis, S., G.S., S.: State estimation via sensor modeling for helicopter control using an indirect kalman filter. In: IEEE/RSJ International Conference

on Intelligent Robots and Systems. (1999)

Gavrilets, V.: Autonomous Aerobatic Manouvering of Miniature. PhD thesis, Massachusetts Institute of Technology (2003)

van der Merwe, R., Wan, E.A.: Sigma-point kalman filters for integrated navigation. In: 60th Annual Meeting of The Institute of Navigation (ION). (2004)

van der Merwe, R., Wan, E.A.: The square-root unscented kalman filter for state and parameter-estimation. In: International Conference on Acoustics, Speech, and

Signal Processing. (2001)

Mettler, B., Tischler, M., Kanade, T.: System identification of a model-scale helicopter. Technical Report CMU-RI-TR-00-03, Robotics Institute, Carnegie Mellon

University, Pittsburgh, PA (2000)

La Civita, M., Messner, W.C., Kanade, T.: Modeling of small-scale helicopters with integrated first-principles and system-identification techniques. In: American helicopter society 58th annual forum. (2002)

Abbeel, P., Ganapathi, V., Ng, A.Y.: Modeling vehicular dynamics, with application to modeling helicopters. In: Neural Information Processing Systems. (2005)

Nolfi, S., Floreano, D.: Evolutionary robotics. MIT Press, Cambridge, MA (2000)

Togelius, J., Lucas, S.M.: Evolving controllers for simulated car racing. In: Proceedings of the Congress on Evolutionary Computation. (2005)

Togelius, J., Lucas, S.M.: Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of cellz. In: Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games CIG05. (2005) 37–43

Ng, A., Kim, H., Jordan, M., Sastry, S., Ballianda, S.: Autonomous helicopter flight via reinforcement learning. Advances in Neural Information Processing Systems (2004)

Ng, A., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., Liang, E.: Autonomous inverted helicopter flight via reinforcement learning. In: Proceedings of the International Symposium on Experimental Robotics. (2004)

De Nardi, R., Togelius, J., Holland, O., Lucas, S.: Neural networks for helicopter control: Why modularity matters. In: IEEE Congress on Evolutionary Computation. (2006)

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