SVC-A2C - Actor Critic Algorithm to Improve Smart Vacuum Cleaner
Resumo
This work present a new approach to develop a vacuum cleaner. This use actor-critic algorithm. We execute tests with three other algoritms to compare. Even that, we develop a new simulator based on Gym to execute the tests.
Palavras-chave:
reinforcement learning, actor-critic, vaccum cleaner
Referências
Hongjun Zhou and Shigeyuki Sakane. Sensor planning for mobile robot localization—a hierarchical approach using a bayesian network and a particle filter. IEEE Transactions on Robotics, 24(2):481–487, 2008.
Kazi Mahmud Hasan, Khondker Jahid Reza, et al. Path planning algorithm development for autonomous vacuum cleaner robots. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pages 1–6. IEEE, 2014.
Iris Wieser, Alberto Viseras Ruiz, Martin Frassl, Michael Angermann, Joachim Mueller, and Michael Lichtenstern. Autonomous robotic slam-based indoor navigation for high resolution sampling with complete coverage. In 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pages 945–951. IEEE, 2014.
Andreas Gylling and Emil Elmarsson. Improving robotic vacuum cleaners: Minimising the time needed for complete dust removal, 2018.
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas. Sample efficient actor-critic with experience replay. arXiv preprint arXiv:1611.01224, 2016.
John J Leonard and Hugh F Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transactions on robotics and Automation, 7(3):376–382, 1991.
Mengfan Li, Chuanjiang Wang, Zhiqiang Chen, Xiao Lu, Meihua Wu, and Pengliang Hou. Path planning of mobile robot based on genetic algorithm and gene rearrangement. In 2017 Chinese Automation Congress (CAC), pages 6999–7004. IEEE, 2017.
Zhongmin Wang and Zhu Bo. Coverage path planning for mobile robot based on genetic algorithm. In 2014 IEEE Workshop on Electronics, Computer and Applications, pages 732–735. IEEE, 2014.
Mohamed Amine Yakoubi and Mohamed Tayeb Laskri. The path planning of cleaner robot for coverage region using genetic algorithms. Journal of innovation in digital ecosystems, 3(1):37–43, 2016.
Sebastian Thrun, Dieter Fox, Wolfram Burgard, and Frank Dellaert. Robust monte carlo localization for mobile robots. Artificial intelligence, 128(1-2):99–141, 2001.
Sven Koenig, Reid Simmons, et al. Xavier: A robot navigation architecture based on partially observable markov decision process models. Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pages 91–122, 1998.
Peng Zhou, Zhong-min Wang, Zhen-nan Li, and Yang Li. Complete coverage path planning of mobile robot based on dynamic programming algorithm. In 2nd International Conference on Electronic & Mechanical Engineering and Information Technology. Atlantis Press, 2012.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym, 2016.
Kazi Mahmud Hasan, Khondker Jahid Reza, et al. Path planning algorithm development for autonomous vacuum cleaner robots. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pages 1–6. IEEE, 2014.
Iris Wieser, Alberto Viseras Ruiz, Martin Frassl, Michael Angermann, Joachim Mueller, and Michael Lichtenstern. Autonomous robotic slam-based indoor navigation for high resolution sampling with complete coverage. In 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pages 945–951. IEEE, 2014.
Andreas Gylling and Emil Elmarsson. Improving robotic vacuum cleaners: Minimising the time needed for complete dust removal, 2018.
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas. Sample efficient actor-critic with experience replay. arXiv preprint arXiv:1611.01224, 2016.
John J Leonard and Hugh F Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transactions on robotics and Automation, 7(3):376–382, 1991.
Mengfan Li, Chuanjiang Wang, Zhiqiang Chen, Xiao Lu, Meihua Wu, and Pengliang Hou. Path planning of mobile robot based on genetic algorithm and gene rearrangement. In 2017 Chinese Automation Congress (CAC), pages 6999–7004. IEEE, 2017.
Zhongmin Wang and Zhu Bo. Coverage path planning for mobile robot based on genetic algorithm. In 2014 IEEE Workshop on Electronics, Computer and Applications, pages 732–735. IEEE, 2014.
Mohamed Amine Yakoubi and Mohamed Tayeb Laskri. The path planning of cleaner robot for coverage region using genetic algorithms. Journal of innovation in digital ecosystems, 3(1):37–43, 2016.
Sebastian Thrun, Dieter Fox, Wolfram Burgard, and Frank Dellaert. Robust monte carlo localization for mobile robots. Artificial intelligence, 128(1-2):99–141, 2001.
Sven Koenig, Reid Simmons, et al. Xavier: A robot navigation architecture based on partially observable markov decision process models. Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pages 91–122, 1998.
Peng Zhou, Zhong-min Wang, Zhen-nan Li, and Yang Li. Complete coverage path planning of mobile robot based on dynamic programming algorithm. In 2nd International Conference on Electronic & Mechanical Engineering and Information Technology. Atlantis Press, 2012.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym, 2016.
Publicado
19/11/2019
Como Citar
ALEIXO, Everton; COLONNA, Juan; BARRETO, Raimundo.
SVC-A2C - Actor Critic Algorithm to Improve Smart Vacuum Cleaner. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 9. , 2019, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 65-70.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2019.8637.
