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Development of rapidly exploring random tree based autonomous mobile robot navigation and velocity predictions using K-nearest neighbors with fuzzy logic analysis
The purpose of autonomous mobile robot navigation is to construct the optimal defended path. In order to ameliorate the accuracy of real time cleaning activity of the mobile robot path planning a rapidly exploring random tree (RRT) algorithm was widely used in larger space environment. This research present the real time cleaning obstacle avoidance in the movement of path using expert system based decision model on machine learning algorithm. Movable robots require a data source, a way to analyze that data, and a way to behave in response to an environment that is changing. The ability to detect and adjust to an unknown situation requires a robust cognitive system. A mobile robot is designed and analysed, which will be autonomously navigated using the RRT navigation algorithm and this will be virtually simulated in a virtual robot experimentation platform. The mobile robot that is designed is tested for its stability. The fuzzy logic analysis is used to predict the mobile robot acceleration and which terrain is most suitable for the robot. Finally using the K-nearest neighbour technique with the labelled accelerometer mobile robot data for velocity prediction. Simulation results decorate the performance of the proposed RRT control system. The duration of travel required for the robot to achieve its objective is calculated, and the findings indicate that operating the robot at 60% of its maximum velocity results is the ideal balance between cleaning effectiveness and time taken.
Development of rapidly exploring random tree based autonomous mobile robot navigation and velocity predictions using K-nearest neighbors with fuzzy logic analysis
The purpose of autonomous mobile robot navigation is to construct the optimal defended path. In order to ameliorate the accuracy of real time cleaning activity of the mobile robot path planning a rapidly exploring random tree (RRT) algorithm was widely used in larger space environment. This research present the real time cleaning obstacle avoidance in the movement of path using expert system based decision model on machine learning algorithm. Movable robots require a data source, a way to analyze that data, and a way to behave in response to an environment that is changing. The ability to detect and adjust to an unknown situation requires a robust cognitive system. A mobile robot is designed and analysed, which will be autonomously navigated using the RRT navigation algorithm and this will be virtually simulated in a virtual robot experimentation platform. The mobile robot that is designed is tested for its stability. The fuzzy logic analysis is used to predict the mobile robot acceleration and which terrain is most suitable for the robot. Finally using the K-nearest neighbour technique with the labelled accelerometer mobile robot data for velocity prediction. Simulation results decorate the performance of the proposed RRT control system. The duration of travel required for the robot to achieve its objective is calculated, and the findings indicate that operating the robot at 60% of its maximum velocity results is the ideal balance between cleaning effectiveness and time taken.
Development of rapidly exploring random tree based autonomous mobile robot navigation and velocity predictions using K-nearest neighbors with fuzzy logic analysis
Int J Interact Des Manuf
Vignesh, C. (author) / Uma, M. (author) / Sethuramalingam, Prabhu (author)
2024-09-01
25 pages
Article (Journal)
Electronic Resource
English
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