Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Rocznik
Tom
Strony
15--19
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
- Department of Computer Science, Biskra University, 07000, Algeria
autor
- 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.
- [8] N. Lassabe, H. Luga and Y. Duthen, “A New Step for Artificial Creatures”. In: 2007 IEEE Symposium on Artificial Life, 2007, 243–250 DOI: 10.1109/ ALIFE.2007.367803.
- [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.
- [12] N. Koenig and A. Howard, “Design and use paradigms for Gazebo, an open-source multi-robot simulator”. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2004, 2149– 2154 DOI: 10.1109/IROS.2004.1389727.
- [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.
- [14] M. T. Hagan, H. B. Demuth and M. H. Beale, Neural network design, Boston: PWS Pub, 1996.
- [15] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, 2002, 182–197 DOI: 10.1109/4235.996017.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0f7f56d5-82b6-480b-ac66-290a9ca702ae