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New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

机译：基于神经网络架构的杂乱环境中基于行为的移动机器人导航新算法

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Purpose - This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach - Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings - Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value - The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.
机译：目的-这项研究涉及一种使用前馈反向传播神经网络传感器-执行器控制技术的，在未知环境中具有障碍的基于行为的轮式移动机器人本地导航的在线路径规划技术。这项研究的目的是找到在混乱环境中移动机器人朝目标移动的非碰撞路径。设计/方法/方法-神经网络体系结构的输入层是不同的跨栏距离信息，这些信息是由一系列配备的传感器获取的，而输出层是转弯角度（电动机控制）。以这种方式，有效地训练了移动机器人以在环境中自主移动。研究结果-计算机仿真和实时实验结果表明，所提出的神经网络控制器可以改善在混乱和未知环境中的导航性能。原创性/价值-与先前开发的模型相比，所提出的神经网络控制器可提供更好的结果（就路径长度而言），从而验证了所提出体系结构的有效性。

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