Search by:
The method of planning the movement of industrial work using an intelligent system.
Full text (PDF)
UDC: 004.8 (045)
Publication Language: Ukrainian
Stuc. intelekt. 2023; 28(3):139-146
Abstract: The paper considered the processes of planning and deployment of robot movement by developing an approach to creating a system based on neural networks. A system is proposed that can perceive the environment and controls the movement of the robot by generating correct control commands. For this purpose, 3 tasks were solved, namely, the analysis of the environment in order to determine its features, the determination of the trajectory in order to neutralize the collision, and the determination of controlled influences for the executive bodies in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks is proposed. The proposed approach is compared with existing approaches on key parameters, such as the execution time of the planned movement and the time of calculating the movement trajectory.
Keywords: neural networks, motion planning system, intelligent system
References:
- Siciliano, B. and Sciavicco, L. (2016). Robotics: Modelling, Planning and Control. Springer. http://doi.org/10.1007/978-1-84628-642-1
- Orr, James & Dutta, Ayan. (2023). Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey. https://doi.org/10.3390/s23073625
- Rivlin, O. (2020, May 5). Generalized Planning With Deep Reinforcement Learning. arXiv.org. https://arxiv.org/abs/2005.02305
- Niku, S.B. (2010). Industrial Robotics: Programming, Simulation and Applications. John Wiley & Sons. https://doi.org/10.5772/40
- LaValle, S. M. (2006). Planning algorithms. Cambridge University Press. https://doi.org/10.1017/CBO9780511546877
- Elhaki, O. and Shojaei, K. (2022). Outputfeedback robust saturated actor–critic multilayer neural network controller for multi-body electrically driven tractors with n-trailer guaranteeing prescribed output constraints. Robot. Aut. Syst. 154, 104106. https://doi.org/10.1016/j.robot.2022.104106
- Jamshidi, Somayeh, Mirzaei, Mehdi & Malekzadeh, Maryam. (2023). Applied nonlinear control of spacecraft simulator with constraints on torque and momentum of reaction wheels. ISA Transactions. http://doi.org/10.1016/j.isatra.2023.03.027
- Wong, C.C., Chen, C.J., Wong, K.Y., Feng, H.M. (2023). Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree. Sensors, 23(10), 4814. https://doi.org/10.3390/s23104814