Abstract
The aim of this paper is to establish an object fetching way formed by a monocular vision unit through the analysis of robot target fetching based on vision. The position distance between the target object and the robot by an NAO robot video camera is measured roughly; however, it is not necessary to measure precisely. Compared with the general steady object fetching way, it is easy to calculate and realize in the algorithm by this paper’s vision fetching way and can avoid the problems generated by the general steady object fetching way, including having difficulty in locating a target, a complicated and difficult image calibration, a high requirement for precision, and so on. There are five different fetching ways including the vertical, horizontal, holding by the two-hand, independently and respectively fetching by the two-hands, and holding the book by two-hands fetching modes. It is proved by the test that this paper’s fetching way has a good recognition capacity, can keep the fetching processing stable, and successfully fetches the various objects by a control approach with positive and inverse kinematics.
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References
Aldana-Murillo NG, Hayet J-B, Becerra H (2018) Comparison of local descriptors for humanoid robots localization using a visual bag of words approach. Intelligent Automation and Soft Computing 24(3):471–481
Aldebaran Robotics (2012) NAO datasheet H25—corporate—Aldebaran Robotics| discover NAO.
Bay H, Tuytelaars T, Gool LV (2006) SURF: Speeded-up robust features. European Conference on Computer Vision, ECCV 2006: Computer Vision – ECCV, pp 404–417
Becerra H (2014) Fuzzy visual control for memory-based navigation using the trifocal tensor. Intelligent Automation and Soft Computing 20(2):245–262
Buschmann T, Lohmeier S, Ulbrich H (2009) Humanoid robot lola: design and walking control. Journal of Physiology-Paris 103:141–148
Carbonera JL, Olszewska JI (2019) Local-Set Based-on Instance Selection Approach for Autonomous Object Modelling. Int J Adv Comput Sci Appl (IJACSA) 10:12
Chen MY (2018) The SLAM algorithm for multiple robots based on parameter estimation. Intelligent Automation and Soft Computing 24(3):593–607
Choi T, Jin S, Lee J (2006) Implementation of a robot actuated by artificial aneumatic muscles. International Joint Conference on SICE-ICASE. Piscataway, NJ, US: IEEE, 4733–4737
Chou Y-C, Nakajima M (2016) Particle filter planar target tracking with a monocular camera for mobile robots. Intelligent Automation and Soft Computing 23(1):117–125
Gu Y, Zhang W (2011) QR code recognition based on image processing. International Conference on Information Science and Technology IEEE:733–736
Huang Y-L, Huang S-P et al (2017) A 3D vision based object grasping posture learning system for home service robots. International Conference on Systems, Man, and Cybernetics:2690–2695
Jafri AR, Huang Q, Yang J (2008) Motion planning of humanoid robot for obstacle negotiation. Journal of Beijing Institute of Technology 4(17):439–444
Jarfi A R, Huang Q, Zhang L, et al. (2006) Realization and trajectory planning for obstacle stepping over by humanoid robot BHR-2. IEEE International Conference on Robotics and Biomimetics. Piscataway, NJ, US: IEEE, : 1384–1389
Jean-Christophe PL, Olivier B, Jean-Guy F (2012) From human motion captures to humanoid spatial coordination. Int J Human Robot, 9(3):1250019
Zhu H, Yi H, Chellali R, Feng L (2018) Object recognition and localization algorithm base on NAO Robot, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp 483–486
Kim J, Park W, Lee J, et al. (2005) System design and dynamic walking of humanoid robot KHR-2. Proceedings of the IEEE International Conference on Robotics and Automation. Piscataway, NJ, US: IEEE, 1431–1436
Lichocki M, Rodrigues L A Gaussian-Biased Heuristic for Stochastic Sampling-Based 2D Trajectory Planning Algorithms, 2020 European control conference (ECC). Saint Petersburg, Russia: IEEE 2020:1949–1954
Liu Z, Chen J, Lan F, Xia H (2020) Methodology of hierarchical collision avoidance for high-speed self-driving vehicle based on motion-decoupled extraction of scenarios. IET Intell Transp Syst 14(3):172–181
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Ma H X, Li G, Wang J (2009) Humanoid walking pattern modification based on foot-ground equivalent contact control. IEEE International Conference on Robotics and Biomimetics. Piscataway, NJ, US: IEEE, : 457–462
Mao Y (2011) Location and tracking algorithm based on BP neural network. Computer Engineering and Applications 47(20):238–240
Ogura Y, Aikawa H (2006) Development of a new humanoid robot WABIAN-2. Proceedings of the IEEE International Conference on Robotics and Automation. Piscataway, NJ, US: IEEE, 76–77
Olszewska JI (2015) Where is My Cup? - Fully automatic detection and recognition of textureless objects in real-world images. International Conference on Computer Analysis of Images and Patterns CAIP: Computer Analysis of Images and Patterns, pp 501–512
Olszewska J (2019) Designing transparent and autonomous intelligent vision systems, proceedings of the 11th international conference on agents and artificial intelligence - volume 2: ICAART, 850–856
Park JH (2003) Fuzzy-logic zero-moment-point trajectory generation for reduce trunk motions of biped robots. Fuzzy Sets Syst 1(34):189–203
Park W, Kim J, Oh J (2005) Mechanical design of humanoid robot platform KHR-3(KAIST Humanoid Robot-3: HUBO). Proceedings of the 5th IEEE-RAS International Conference on Humanoid Robots. Piscataway, NJ, US: IEEE, 321–326
Peter S, Jaroslav T, Zlatko F et al (2011) Development of cognitive capabilities for robot NAO in center for intelligent technologies in Kosice. 2nd international conference on cognitive info communications. Piscataway, NJ, US: IEEE 3(16):1–5
Rosten E, Drummond T (2010) Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119
Shabrina N, Li D, Isshiki T (2020) Small Area Fingerprint Verification using Deep Convolutional Neural Network. 2020 International Conference on Intelligent Engineering and Management (ICIEM), London, UK: IEEE, : 1–6
Shen J, Gans N (2018) Robot-to-human feedback and automatic object grasping using an RGB-D camera–projector system. Robotica 36(2):1–20
Singh AK, Nandi GC (2016) NAO humanoid robot: analysis of calibration techniques for robot sketch drawing. Robotics & Autonomous Systems 79:108–121
Tang Y, et al. (2014) "A Target Positioning Method of Monocular Visual Based on NAO Robot." J Changchun Univ Sci Technol
Wang J, Sheng T, Ma H, et al. (2006) Design and dynamic walking control of humanoid robot blackmann. The Sixth World Congress on Intelligent Control and Automation. Piscataway, NJ, US: IEEE, : 8848–8852
Xu D et al (2009) Ceiling-based visual positioning for an indoor Mobile robot with monocular vision. IEEE Trans Ind Electron 56(5):1617–1628
Zheng X (2013) “Research on intelligent grasping Technology of Humanoid Robot Based on visual servo”. Master thesis: Northeastern University
Zhong Q B, Pan Q S, Hong B R, et al. (2008) Design and implementation of humanoid robot HIT-2. International Conference on Robotics and Biomimetics. Piscataway, NJ, US: IEEE, : 967–970
Zhuo S, Jiao S, Zou W, Wang N, Li X (2020) Shift, rotation and scale invariant optical fingerprint verification system with double random phase encoding. The Journal of Engineering 13(7):476–481
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The author deeply acknowledges Mr. Li, Yi-Jin initial test support at first rough model.
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Juang, LH. Humanoid robot fetching objects using monocular vision unit. Multimed Tools Appl 82, 6747–6767 (2023). https://doi.org/10.1007/s11042-022-13602-8
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DOI: https://doi.org/10.1007/s11042-022-13602-8