Real-time Coordinate Estimation for Self-Localization of the Humanoid Robot Soccer BarelangFC
Abstract
In implementation, of the humanoid robot soccer consists of more than three robots when played soccer on the field. All the robots needed to be played the soccer as human done such as seeking, chasing, dribbling and kicking the ball. To do all of these commands, it is required a real-time localization system so that each robot will understand not only the robot position itself but also the other robots and even the object on the field’s environment. However, in real-time implementation and due to the limited ability of the robot computation, it is necessary to determine a method which has fast computation and able to save much memory. Therefore, in this paper we presented a real-time localization implementation method using the odometry and Monte Carlo Localization (MCL) method. In order to verify the performance of this method, some experiment has been carried out in real-time application. From the experimental result, the proposed method able to estimate the coordinate of each robot position in X and Y position on the field.
Downloads
References
O. Sugiyama, R. Kojima and K. Nakadai, "Interactive interface to optimize sound source localization based on microphone array with coarse-to-fine tuning for humanoids," 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), Seoul, 2015, pp. 825-830,
doi: 10.1109/HUMANOIDS.2015.7363449.
Kumagai et al., "Complementary integration framework for localization and recognition of a humanoid robot based on task-oriented frequency and accuracy requirements," 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birmingham, 2017, pp. 683-688,
doi: 10.1109/HUMANOIDS.2017.8246946.
M. Fourmy, D. Atchuthan, N. Mansard, J. Solà and T. Flayols, "Absolute humanoid localization and mapping based on IMU Lie group and fiducial markers," 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, ON, Canada, 2019, pp. 237-243, doi: 10.1109/Humanoids43949.2019.9035005.
M. F. Fallón, M. Antone, N. Roy and S. Teller, "Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing," 2014 IEEE-RAS International Conference on Humanoid Robots, Madrid, 2014, pp. 112-119, doi: 10.1109/HUMANOIDS.2014.7041346.
V. Sushrutha Raghavan, D. Kanoulas, C. Zhou, D. G. Caldwell and N. G. Tsagarakis, "A Study on Low-Drift State Estimation for Humanoid Locomotion, Using LiDAR and Kinematic-Inertial Data Fusion," 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China, 2018, pp. 1-8, doi: 10.1109/HUMANOIDS.2018.8624953.
T. Zhang, E. Uchiyama and Y. Nakamura, "Dense RGB-D SLAM for Humanoid Robots in the Dynamic Humans Environment," 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China, 2018, pp. 270-276, doi: 10.1109/HUMANOIDS.2018.8625019.
R. Sheikh, S. OBwald and M. Bennewitz, "A Combined RGB and Depth Descriptor for SLAM with Humanoids," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, 2018, pp. 1718-1724, doi: 10.1109/IROS.2018.8593768.
Antoine Rioux, Wael Suleiman, Autonomous SLAM based humanoid navigation in a cluttered environment while transporting a heavy load, Robotics and Autonomous Systems, Volume 99, 2018, Pages 50-62, ISSN 0921-8890, https://doi.org/10.1016/j.robot.2017.10.001. (http://www.sciencedirect.com/science/article/pii/S0921889015303043)
J. Delfin, H. M. Becerra and G. Arechavaleta, "Humanoid localization and navigation using a visual memory," 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, 2016, pp. 725-731, doi: 10.1109/HUMANOIDS.2016.7803354.
Josafat Delfin, Héctor M. Becerra, Gustavo Arechavaleta, Humanoid navigation using a visual memory with obstacle avoidance, Robotics and Autonomous Systems, Volume 109, 2018, Pages 109-124, ISSN 0921-8890, https://doi.org/10.1016/j.robot.2018.08.010.
H. Minakata et al., "A method of single camera robocup humanoid robot localization using cooperation with walking control," 2008 10th IEEE International Workshop on Advanced Motion Control, Trento, 2008, pp. 50-55, doi: 10.1109/AMC.2008.4516040.
M. N. Sudin, M. F. Nasrudin and S. N. H. S. Abdullah, "Humanoid localisation in a robot soccer competition using a single camera," 2014 IEEE 10th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, 2014, pp. 77-81, doi: 10.1109/CSPA.2014.6805724.
L. Guohua, X. Xiandong, Y. Xiang, W. Yadong and Q. Tianwei, "An Indoor Localization Method for Humanoid Robot Based on Artificial Landmark," 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, 2015, pp. 1854-1857, doi: 10.1109/IMCCC.2015.394.
Mahdi Fakoor, Amirreza Kosari, Mohsen Jafarzadeh, Humanoid robot path planning with fuzzy Markov decision processes, Journal of Applied Research and Technology, Volume 14, Issue 5, 2016, Pages 300-310, ISSN 1665-6423, https://doi.org/10.1016/j.jart.2016.06.006.(http://www.sciencedirect.com/science/article/pii/S1665642316300700)
X. Xu, B. Hong and Y. Guan, "Humanoid robot localization based on hybrid map," 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, 2017, pp. 509-514, doi: 10.1109/SPAC.2017.8304331.
B. Tian, Chuen-Leong Ng and C. Chew, "Self-localization of humanoid robots with fish-eye lens in a soccer field," 2010 IEEE Conference on Robotics, Automation and Mechatronics, Singapore, 2010, pp. 522-527, doi: 10.1109/RAMECH.2010.5513138.
H. Li et al., "A humanoid robot localization method for biped navigation in human-living environments," 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, 2015, pp. 540-544, doi: 10.1109/CYBER.2015.7287997.
D. Maier, A. Hornung and M. Bennewitz, "Real-time navigation in 3D environments based on depth camera data," 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, 2012, pp. 692-697, doi: 10.1109/HUMANOIDS.2012.6651595.
Y. Omori, T. Furukawa, T. Ishikawa and M. Inaba, "Humanoid Vision Design for Object Detection, Localization and Mapping in Indoor Environments," 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, 2018, pp. 1-6, doi: 10.1109/SSRR.2018.8468604.
G. Oriolo, A. Paolillo, L. Rosa and M. Vendittelli, "Vision-based Odometric Localization for humanoids using a kinematic EKF," 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, 2012, pp. 153-158, doi: 10.1109/HUMANOIDS.2012.6651513.
G. Oriolo, A. Paolillo, L. Rosa and M. Vendittelli, "Vision-based trajectory control for humanoid navigation," 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Atlanta, GA, 2013, pp. 118-123, doi: 10.1109/HUMANOIDS.2013.7029965.
Y. Minami Shiguematsu, M. Brandao, K. Hashimoto and A. Takanishi, "Effects of Biped Humanoid Robot Walking Gaits on Sparse Visual Odometry Algorithms," 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China, 2018, pp. 160-165, doi: 10.1109/HUMANOIDS.2018.8625015.
R. Carrillo Mendoza, P. Vera Bustamante, Brian, E. Hernández Castillo and J. M. Ibarra Zannatha, "3D self-localization for humanoid robots using view regression and odometry," 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Mexico City, 2015, pp. 1-5, doi: 10.1109/ICEEE.2015.7357988.
Saeed Saeedvand, Hadi S. Aghdasi, Jacky Baltes, Novel lightweight odometric learning method for humanoid robot localization, Mechatronics, Volume 55, 2018, Pages 38-53, ISSN 0957-4158, https://doi.org/10.1016/j.mechatronics.2018.08.007.(http://www.sciencedirect.com/science/article/pii/S0957415818301338)
Wei Hong, C. Zhou and Y. Tian, "Robust Monte Carlo Localization for humanoid soccer robot," 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, 2009, pp. 934-939, doi: 10.1109/AIM.2009.5229889.
I. Nagi, W. Adiprawita and K. Mutijarsa, "Vision-based Monte Carlo localization for RoboCup Humanoid Kid-Size League," 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2014, pp. 1433-1438, doi: 10.1109/ICARCV.2014.7064526.
A. Muzio, L. Aguiar, M. Maximo and S. Pinto, "Monte Carlo Localization with Field Lines Observations for Simulated Humanoid Robotic Soccer," in 2016 XIII Latin-American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR), Recife, 2016 pp. 334-339. doi: 10.1109/LARS-SBR.2016.63https://doi.ieeecomputersociety.org/10.1109/LARS-SBR.2016.63
A. C. Almeida, A. H. R. Costa and R. A. C. Bianchi, "Vision-based monte-carlo localization for humanoid soccer robots," 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), Curitiba, 2017, pp. 1-6, doi: 10.1109/SBR-LARS-R.2017.8215310.do
Hartfill, Judith. (2019). Feature-Based Monte Carlo Localization in the RoboCup Humanoid Soccer League. 10.13140/RG.2.2.19044.73602.
Susanto, F. Azmi and R. Analia, "Trigonometry Algorithm for Ball Heading Prediction of Barelang-FC Goal Keeper," 2018 International Conference on Applied Engineering (ICAE), Batam, 2018, pp. 1-6, doi: 10.1109/INCAE.2018.8579361.
Susanto, Febri Alwan Putra and R. Analia, "XNOR-YOLO: The High Precision of the Ball and Goal Detecting on the Barelang-FC Robot Soccer," 2020 International Conference on Applied Engineering (ICAE), Batam, 2020 (virtual presentation on 7th -8th October 2020)
RoboCup Humanoid Technical Committee. Laws of the Game 2019.
http://www.robocuphumanoid.org/wp-content/uploads/ RCHL-2019-Rules-final.pdf, 2019.
