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首页> 外文期刊>Robotica >Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach
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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

机译：反向传播神经网络集成改进DAYANI方法的未知地形两轮自平衡机器人导航控制分析

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摘要

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.
机译：本文讨论了在杂乱无章的环境中，反向传播神经网络集成改进的DAYANI方法对两轮自平衡机器人的路径控制的开发和实现。已经提出了一个五层的反向传播神经网络，以考虑从改进的DAYANI方法获得的七个导航参数来找出各种权重因子的强度。权重因子的强度是使用神经技术找到的，其输入参数包括沿目标方向的可见相交障碍物数量，从传感器获得的最小可见前障碍物距离，在左侧可见障碍物范围内的最小左侧障碍物距离。机器人，左侧障碍物距离的平均值，在机器人可见的右侧范围内的最小右侧障碍物距离，右侧障碍物距离的平均值以及距机器人可能的下一个位置的目标距离。为了验证所提出控制器的鲁棒性，进行了仿真和实验练习之间的比较。此外，通过在完全相似的测试场景中，通过对其他现有技术与当前技术获得的结果进行比较分析，验证了所提出控制器的真实性，并见证了结果的增强。

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