Robot localization is the computational procedure used to determine the exact spatial coordinates of a mobile robot relative to its surrounding environment. The acquisition of localization is an essential ability for an autonomous robot, since it plays a fundamental role in enabling the robot to accurately ascertain its own location. The understanding of the robot's spatial coordinates is an essential need for the robot to make well-informed decisions on its subsequent actions. In a normal scenario of robot localization, a map of the surrounding region is available, and the robot is equipped with sensors that facilitate the examination of the environment and the monitoring of its own motion. The difficulty of localization thereafter becomes the job of calculating the precise orientations and position of the robot inside the map via the use of data gathered from these sensors. To adequately handle the existence of noisy observations, it is essential for robot localization algorithms to possess the capacity to not only provide an assessment of the robot's position, but also to quantify the degree of uncertainty associated with this estimation of location. The main purpose of this study is to formulate a methodology that combines neural networks with boosting techniques to enhance the effectiveness of robot localization. The suggested methodology entails the selection and validation of neural network topologies, the extraction of pertinent features, and the use of boosting methods to augment classification performance. The objective is to get accurate and dependable robot localization via the use of these strategies.
Keywords
Robot Localization, Convolutional Neural Networks, Long Short-Term Memory, Long-Term Recurrent Convolutional Network, Support Vector Machine.
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Derek Stewart
Derek Stewart
Faculty of Sciences, University of Tizi Ouzou, Algeria.
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Cite this article
Rob Argent and Derek Stewart, “Enhancing Robot Localization Accuracy through Neural Networks and Boosting Techniques”, Journal of Robotics Spectrum, vol.1, pp. 155-164, 2023. doi: 10.53759/9852/JRS202301015.
