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• W2912659361 abstract "Summary The present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed." @default.
• W2912659361 created "2019-02-21" @default.
• W2912659361 creator A5052764209 @default.
• W2912659361 creator A5069237189 @default.
• W2912659361 date "2019-02-15" @default.
• W2912659361 modified "2023-09-26" @default.
• W2912659361 title "Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach" @default.
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• W2912659361 doi "https://doi.org/10.1017/s0263574718001558" @default.
• W2912659361 hasPublicationYear "2019" @default.
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