Abstract
Insect-Scale Flapping-Wing Micro-Air Vehicles (FW-MAVs) may be particularly sensitive to degradation of pose and position control caused by ongoing or pre-existing damage to the airframes. Previous work demonstrated that in-flight recovery of sufficient pose and position control precision via use of an adaptive oscillator component inside traditional SISO controllers. This work will replace previously used oscillator learning algorithms with a hyperplane sampling Evolutionary Algorithm (EA) that employs cross-vehicle islands-of-fitness. It will be demonstrated that this strategy allows swarms of vehicles to cooperatively, and more quickly, find and correct for simulate manufacturing errors that appear in all vehicles – even in the presence of randomized vehicle specific errors that are not common to all vehicles in the swarm. The paper will present specific simulation results demonstrating efficacy of this scheme and discussion of future applications of islands-of-fitness methods in this problem domain.
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References
Wood, R.: The first takeoff of a biologically-inspired at-scale robotic insect. IEEE Trans. on Robotics 24(11), 341–347 (2008)
Doman, D., Oppenheimer, M., Sigthorsson, D.: Dynamics and control of a minimally actuated biomimetic vehicle: part I – aerodynamic model. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2009)
Oppenheimer, M., Doman, D., Sigthorsson, D.: Dynamics and control of a minimally actuated biomimetic vehicle: part II – control. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2009)
Doman, D., Oppenheimer, M., Bolender, M., Sigthorsson, D.: Altitude control of a single degree of freedom flapping wing micro air vehicle. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2009)
Gallagher, J., Doman, D., Oppenheimer, M.: Practical in-flight altitude control learning in a flapping-wing micro air vehicle (submitted)
Gallagher, J., Oppenheimer, M.: An improved evolvable oscillator for all flight mode control of an insect-scale flapping-wing micro air vehicle. In: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (2011)
Gallagher, J., Oppenheimer, M.: Cross-layer learning in an evolvable oscillator for in-flight control adaptation of a flapping-wing micro air vehicle. In: The 45th Asilomar Conference on Signals, Systems, and Computers (2011)
Gallagher, J., Doman, D., Oppenheimer, M.: The technology of the gaps: an evolvable hardware synthesized oscillator for the control of a flapping-wing micro air vehicle. IEEE Trans. on Evolutionary Computation (in press)
Greenwood, G., Tyrrell, A.: Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems. IEEE Press (2005)
Harik, G., Lobo, F., Goldberg, D.: The compact genetic algorithm. IEEE Transactions on Evolutionary Computation 2(2), 287–297 (1999)
Aporntewan, C., Chongistivatana, P.: A hardware implementation of the compact genetic algorithm. In: Proc. of the 2001 Congress on Evolutionary Computation, pp. 624–629 (2001)
Gallagher, J., Vigraham, S., Kramer, G.: A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Transactions on Evolutionary Computation 8(2), 111–126 (2004)
Cohoon, J., Hedge, S., Martin, W., Richards, D.: Punctuated equilibria: a parallel genetic algorithm. In: Proc. of the 2nd International Conference on Genetic Algorithms and Their Applications (1987)
Timmerman, K., Gallagher, J.: A hardware compact genetic algorithm adaptive oscillator for hover improvement in an insect-scale flapping-wing micro air vehicle. In: Proc. of the National Aerospace and Electronics Conference (2012)
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Gallagher, J.C. (2013). An Islands-of-Fitness Compact Genetic Algorithm Approach to Improving Learning Time in Swarms of Flapping-Wing Micro Air Vehicles. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_82
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DOI: https://doi.org/10.1007/978-3-642-37374-9_82
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