UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

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.

[1]  S. Shankar Sastry,et al.  Autonomous Helicopter Flight via Reinforcement Learning , 2003, NIPS.

[2]  Chris Melhuish,et al.  Autonomous Minimalist Following In Three Dimensions: A Study with Small-Scale Dirigibles , 2001 .

[3]  Julian Togelius,et al.  Evolving controllers for simulated car racing , 2005, 2005 IEEE Congress on Evolutionary Computation.

[4]  B. Crowther,et al.  Flocking of autonomous unmanned air vehicles , 2003, The Aeronautical Journal (1968).

[5]  D. Floreano,et al.  Evolutionary Robotics: The Biology,Intelligence,and Technology , 2000 .

[6]  George J. Pappas,et al.  Stable flocking of mobile agents, part I: fixed topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[7]  Marco,et al.  Modeling of Small-Scale Helicopters with Integrated First-Principles and System-Identification Techniques , 2002 .

[8]  Pieter Abbeel,et al.  Learning vehicular dynamics, with application to modeling helicopters , 2005, NIPS.

[9]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[10]  Julian Togelius,et al.  Evolution of Neural Networks for Helicopter Control: Why Modularity Matters , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  Maja J. Mataric,et al.  Interaction and intelligent behavior , 1994 .

[12]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[13]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[14]  Baris Fidan,et al.  Coordination and Control of Multi-agent Dynamic Systems: Models and Approaches , 2006, Swarm Robotics.

[15]  Kalman FilterMyungsoo Juny,et al.  State Estimation via Sensor Modeling for Helicopter Control usingan Indirect , 2007 .

[16]  George J. Pappas,et al.  Stable flocking of mobile agents part I: dynamic topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[17]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[18]  Ben Tse,et al.  Autonomous Inverted Helicopter Flight via Reinforcement Learning , 2004, ISER.

[19]  Rudolph van der Merwe,et al.  Sigma-Point Kalman Filters for Integrated Navigation , 2004 .

[20]  Daniel Preston,et al.  Swarming/Flocking and Collision Avoidance for Mass Airdrop of Autonomous Guided Parafoils , 2005 .

[21]  Ian Darrell Kelly.  FLOCKING BY THE FUSION OF SONAR AND ACTIVE INFRARED SENSORS ON PHYSICAL AUTONOMOUS MOBILE ROBOTS , 1996 .

[22]  Vladislav Gavrilets,et al.  Autonomous aerobatic maneuvering of miniature helicopters , 2003 .

[23]  John Woods,et al.  Beyond swarm intelligence: the UltraSwarm , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[24]  Julian Togelius,et al.  Forcing Neurocontrollers to Exploit Sensory Symmetry Through Hard-wired Modularity in the Game of Cellz , 2005, CIG.