Development of Advance Machine Learning (ML) Strategies for Enhanced Mobile Robot Control
DOI:
https://doi.org/10.48047/epjxw841Keywords:
Mobile Robot, Machine Learning, Reinforcement Learning, Supervised Learning, Robotic.Abstract
Recently the demand for intelligent robotic systems has been increasing in many fields, necessitating advanced machine learning (ML) strategies to make mobile robot control more effective. This study deals with building robust ML-based methods to enhance the key features of a vehicle, including but not limited to navigation, complexity avoidance, and object detection in various functional settings. The methodology presents problem definition and dataset acquisition, simulating synthetic data (from both Gazebo and Webots) and real data collected from LiDAR, camera, and IMU sensors to provide robustness and generalization. It will also require data pre-processing techniques such as Kalman filtering and feature extraction to clean the data and reduce noise before sending it to the model for training.
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References
G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, pp. 405–426, Oct. 2021, doi: 10.1016/j.ejor.2021.01.019.
R. Raj and A. Kos, “A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives,” Appl. Sci., vol. 12, no. 14, p. 6951, Jul. 2022, doi: 10.3390/app12146951.
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