International Journal of Neural Network, 2021, 2(2); doi: 10.38007/NN.2021.020204.
Yanjiang Wang
Zhejiang Sci-Tech University, Hangzhou, China
With the rapid improvement of technologies such as machine learning, artificial intelligence and new applications, robots have gradually evolved from tools that can only complete simple repetitive tasks to applications that serve very efficient and intelligent services, and can be customized according to customer needs. Robot interaction is an important part of robotics as the needs of users and the improvement of ever-changing practical applications. The purpose of this paper is to analyze the robot interaction based on support vector machine. In the experiment, a database is established, and the robot interaction is investigated and analyzed from the quantitative evaluation results and the analysis and discussion of the experimental results by using the curve of recognition rate and recall rate.
Support Vector Machine, Robot Interaction Analysis, Human-computer Interaction Technology Analysis, Database
Yanjiang Wang. Robot Interaction Analysis Based on Support Vector Machine. International Journal of Neural Network (2021), Vol. 2, Issue 2: 25-32. https://doi.org/10.38007/NN.2021.020204.
[1] Tsai C C, Chang C W, Tao C W. Vision-Based Obstacle Detection for Mobile Robot in Outdoor Environment. Journal of Information Science and Engineering. (2018) 34(1):21-34.
[2] Kim S G, Lee Y C, Ahn S S, et al. Autonomous Feeding Robot and its Ultrasonic Obstacle Classification System. Transactions of the Korean Institute of Electrical Engineers. (2018) 67(8):1089-1098.
[3] Park K M, Kim J, Park J, et al. Learning-Based Real-Time Detection of Robot Collisions without Joint Torque Sensors. IEEE Robotics and Automation Letters. (2021) 6(1):103-110.
[4] Bousseta R, Ouakouak I E, Gharbi M, et al. EEG Based Brain Computer Interface for Controlling a Robot Arm Movement through thought. Innovation & Research in Biomedical En. (2018) 39(2):129-135.
[5] Deuerlein C, Langer M, Sener J, et al. Human-Robot-Interaction Using Cloud-Based Speech Recognition Systems. Procedia CIRP. (2021) 97(2):130-135.
[6] Badr A A, Karim A. A Review on Voice-based Interface for Human-Robot Interaction. Iraqi Journal for Electrical and Electronic Engineering. (2020) 16(2):91-102.
[7] Park D, Kang K M, Bae J W, et al. Robot Vision to Audio Description Based on Deep Learning for Effective Human-Robot Interaction. The Journal of Korea Robotics Society. (2019) 14(1):22-30.
[8] Geweid G, Abdallah M A. A New Automatic Identification Method of Heart Failure Using Improved Support Vector Machine Based on Duality Optimization Technique. IEEE Access. (2019) 7(99):149595-149611.
[9] Yeh W C, Jiang Y, Tan S Y, et al. A New Support Vector Machine Based on Convolution Product. Complexity. (2021) (2021) (4):1-19.
[10] Samsudin E. Modeling Student's Academic Performance during Covid-19 Based on Classification in Support Vector Machine. Turkish Journal of Computer and Mathematics Education (TURCOMAT) (2021) 12(5):1798-1804.
[11] Ma H, Honig W, Cohen L, et al. Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems. IEEE Intelligent Systems. (2018) 32(6):6-12.
[12] Sampedro C, Rodriguez-Ramos A, Bavle H, et al. A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. Journal of Intelligent & Robotic Systems. (2019) 95(2):601-627.
[13] Orquin J L, Chrobot N, Grunert K G. Guiding Decision Makers' Eye Movements with (Un)Predictable Object Locations. Journal of Behavioral Decision Making. (2018) 31(3):341-354.
[14] Arvin F, Watson S, Turgut A E, et al. Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging. Journal of Intelligent & Robotic Systems. (2018) 92(4):1-18.
[15] Schlotfeldt B, Thakur D, Atanasov N, et al. Anytime Planning for Decentralized Multirobot Active Information Gathering. IEEE Robotics and Automation Letters. (2018) 3(2):1025-1032.
[16] Matsuda M, Mataki Y, Mutsuzaki H, et al. Immediate Effects of A Single Session of Robot-Assisted Gait Training Using Hybrid Assistive Limb (HAL) For Cerebral Palsy. Journal of Physical Therapy Science. (2018) 30(2):207-212.
[17] Santos M, Diaz-Mercado Y, Egerstedt M. Coverage Control for Multi-Robot Teams with Heterogeneous Sensing Capabilities. IEEE Robotics & Automation Letters. (2018) 3(2):919-925.
[18] Tsang-Kai, Chang, Ankur, et al. Optimal Scheduling for Resource-Constrained Multirobot Cooperative Localization. IEEE Robotics and Automation Letters. (2018) 3(3):1552-1559.