Beyond Barriers: Comparative Insights into Machine Learning Algorithms for Autonomous Mobile Bots in Indoor Environments
Advances in Science, Technology and Innovation, ISSN: 2522-8722, Page: 81-95
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In this paper, the development of an Indoor Autonomous Mobile Bot has been introduced by leveraging an Ultrasonic Sensor and Infrared Sensor, in tandem with a Controller. The Ultrasonic Sensor records the obstacle distance data, while the Infrared Sensor measures the velocities of both wheels. Within this investigative framework, three Machine Learning (ML) algorithms—Adaptive Stochastic Gradient Descent Linear Regression (ASGDLR), Adaptive Coordinate Descent Logistic Regression (ACDLoR), and Adaptive Stochastic Gradient Descent LARS Regression (ASGDLARS)—are implemented for the explicit objective of negotiating obstacles within constrained spatial confines. The findings encapsulate simulation outcomes that scrutinize diverse facets of the Confusion Matrix, alongside the computational derivation of obstacle avoidance percentages in the context of singular obstacle scenarios across varying locomotive speeds.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85210886390&origin=inward; http://dx.doi.org/10.1007/978-3-031-68038-0_12; https://link.springer.com/10.1007/978-3-031-68038-0_12; https://dx.doi.org/10.1007/978-3-031-68038-0_12; https://link.springer.com/chapter/10.1007/978-3-031-68038-0_12
Springer Science and Business Media LLC
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