An adaptive sparse general regression neural network-based force observer for teleoperation system. (February 2023)

• Record Type: Journal Article
• Authors: Pan, Mingzhang; Li, Jing; Yang, Qiye; Wang, Yupeng; Tang, Yu; Pan, Lei; Jiang, Xianbao; Lin, Yizhong; Liang, Ke
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: Force observer -- General regression neural network -- Teleoperation system -- Master–slave​ robot -- Feature selection -- Antlion optimization algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105689
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Restricted by factors such as small robot size and harsh operating environment, the inability to obtain the interaction force between the robot arm and the environment through force sensors has become problematic in promoting the application of teleoperation systems in minimally invasive surgery, nuclear waste cleanup, and other fields. To accurately obtain the interaction force without the force sensors, a force observer based on an adaptive sparse general regression neural network (ASGRNN) is proposed in this paper. The proposed force observer uses a machine learning-based approach to obtain estimated force, thus eliminating the need for the dynamic parameters of the robot arm. Also, an innovative feature selection method incorporating the wrapper method and sparse regularization is proposed to select the input features of the force observer. Secondly, two new criteria are defined to eliminate the useless support vectors in the model. In addition, an improved antlion optimization algorithm (IALO) is proposed to optimize the bandwidth parameters of the model. To verify the performance of the proposed force observer, a 6-degree-of-freedom teleoperation robot experimental platform is built and compared with three existing force estimation models. The results show that the proposed force observer outperforms the existing model in terms of estimation accuracy, and the mean square error (MSE) is at least 35.79% lower than the existing model. In conclusion, this paper provides a feasible and effective force observer for a teleoperation system where force sensors are not applicable and dynamic parameters are non-knowable. Highlights: A new interaction force observer based on machine learning is proposed. The proposed force observer does not require a force sensor and does not rely on any robot dynamics parameters. A new feature selection method is proposed for selecting the input features of the force observer. An improved ant lion optimization algorithm is proposed to optimize the force observer parameters. The proposed force observer can accurately predict the interaction forces between the robot arm and the environment.