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Motion planning and tracking control of a four-wheel independently driven steered mobile robot with multiple maneuvering modes

机译:具有多种机动模式的四轮独立驱动转向移动机器人的运动规划和跟踪控制

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Safe and effective autonomous navigation in dynamic environments is challenging for four-wheel independently driven steered mobile robots (FWIDSMRs) due to the flexible allocation of multiple maneuver modes. To address this problem, this study proposes a novel multiple mode-based navigation system, which can achieve efficient motion planning and accurate tracking control. To reduce the calculation burden and obtain a comprehensive optimized global path, a kinodynamic interior-exterior cell exploration planning method, which leverages the hybrid space of available modes with an incorporated exploration guiding algorithm, is designed. By utilizing the sampled subgoals and the constructed global path, local planning is then performed to avoid unexpected obstacles and potential collisions. With the desired profile curvature and preselected mode, a fuzzy adaptive receding horizon control is proposed such that the online updating of the predictive horizon is realized to enhance the trajectory-following precision. The tracking controller design is achieved using the quadratic programming (QP) technique, and the primal-dual neural network optimization technique is used to solve the QP problem. Experimental results on a real-time FWIDSMR validate that the proposed method shows superior features over some existing methods in terms of efficiency and accuracy.
机译:由于多种机动模式的灵活分配,在动态环境中安全有效的自主导航对于四轮独立驱动的转向移动机器人(FWIDSMR)具有挑战性。为了解决这个问题,本研究提出了一种新型基于模式的导航系统,可以实现高效的运动规划和准确的跟踪控制。为了减少计算负担并获得全面优化的全局路径,设计了一种新的内部外部细胞勘探计划方法,利用了具有掺入探索引导算法的可用模式的混合空间。通过利用采样的子路和构造的全局路径,然后执行局部规划以避免意外的障碍和潜在的碰撞。利用所需的轮廓曲率和预选模式,提出了一种模糊的自适应后退地平线控制,使得实现预测地平线的在线更新以提高轨迹之后的精度。使用二次编程(QP)技术实现跟踪控制器设计,并使用原始 - 双神经网络优化技术来解决QP问题。实验结果对实时FWIDSMR的实验结果验证了所提出的方法在效率和准确性方面显示出一些现有方法的优越特征。

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