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Damage recovery for simulated modular robots through joint evolution of morphologies and controllers

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Języki publikacji
EN
Abstrakty
EN
In order to be fully autonomous, robots have to be resilient so that they can recover from damages and operate for a long period of time with no human assistance. To be resilient, existing approaches propose to change the robots’ behavior using a different control system when a hardware fault or damage occurs. These approaches are used for robots which have fixed morphologies. However, we cannot assume which morphology would be optimal for a given problem and which morphology allows resilience. In the present paper, we introduce a new approach that generates resilient artificial modular robots by evolving the robot morphology along with its controller. We used a multi-objective evolutionary algorithm to optimize two objectives at a time, which are the traveled distance of a damage-free robot and the traveled distance of the same robot with damaged parts. The result of preliminary experiments demonstrates that during evaluation, when robots are deliberately faced to motor failures, the evolution process can optimize and generate new morphologies for which the robot’s behavior is less affected by damage. This makes the robot capable to recover its ability to move forward.
Twórcy
  • Department of Computer Science, Biskra University, 07000, Algeria
  • Department of Computer Science, Biskra University, 07000, Algeria
Bibliografia
  • [1] A. Cully, J. Clune, D. Tarapore and J.-B. Mouret, “Robots that can adapt like animals”, Nature, vol. 521, no. 7553, 2015, 503–507 DOI: 10.1038/nature14422.
  • [2] J. Bongard, V. Zykov and H. Lipson, “Resilient Machines Through Continuous Self-Modeling”, Science, vol. 314, no. 5802, 2006, 1118–1121 DOI: 10.1126/science.1133687.
  • [3] D. Berenson, N. Estevez and H. Lipson, “Hardware evolution of analog circuits for in-situ robotic fault-recovery”. In: 2005 NASA/DoD Conference on Evolvable Hardware (EH’05), 2005, 12–19 DOI: 10.1109/EH.2005.30.
  • [4] S. H. Mahdavi and P. J. Bentley, “Innately adaptive robotics through embodied evolution”, Autonomous Robots, vol. 20, no. 2, 2006, 149–163 DOI: 10.1007/ s10514-006-5941-6.
  • [5] J. C. Bongard, A. Bernatskiy, K. Livingston, N. Livingston, J. Long and M. Smith, “Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability”. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, 2015, 129–136 DOI: 10.1145/2739480.2754750.
  • [6] N. Cheney, J. Bongard, V. SunSpiral and H. Lipson, “Scalable co-optimization of morphology and control in embodied machines”, Journal of The Royal Society Interface, vol. 15, no. 143, 2018 DOI: 10.1098 /rsif.2017.0937.
  • [7] K. Sims, “Evolving Virtual Creatures”. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, USA, 1994, 15–22 DOI: 10.1145 /192161.192167.
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  • [9] A. E. Eiben and J. Smith, “From evolutionary computation to the evolution of things”, Nature, vol. 521, no. 7553, 2015, 476–482 DOI: 10.1038/ nature14544.
  • [10] C. C. Coello, G. B. Lamont and D. A. van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Genetic and Evolutionary Computation Series, Springer US, 2007 DOI: 10.1007/ 978-0-387-36797-2
  • [11] D. Akrour, S. Cussat-Blanc, S. Sanchez, N. Djedi and H. Luga, “Joint evolution of morphologies and controllers for realistic modular robots”. In: 22nd Symposium on Artificial Life and Robotics (AROB 2017), Beppu, Japan, 2017, 57–62.
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  • [13] M. Yim, W. Shen, B. Salemi, D. Rus, M. Moll, H. Lipson, E. Klavins and G. S. Chirikjian, “Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]”, IEEE Robotics Automation Magazine, vol. 14, no. 1, 2007, 43–52 DOI: 10.1109/MRA. 2007.339623.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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