【摘要】 在动态环境下的局部避障是移动机器人的一项基本功能.在各种速度空间方法,如曲率-速率法(CVM)、巷道-曲率法(LCM)和扇区-曲率法(BCM)的基础上,提出了一种适用于未知或部分未知动态环境的局部避障方法.该方法将碰撞预测模型与改进后的BCM有效结合,不仅兼备了CVM的平滑性、LCM的安全性和BCM快速性的优点,而且弥补了各种速度空间寻优方法的不足,使其能够适用于移动机器人在动态环境下的避障与导航.实际机器人的导航实验表明该算法是可行而有效的.更多还原

【Abstract】 Local obstacle avoidance in dynamic environments, as a principal capability for mobile robots, plays an important role in autonomous navigation. A variety of velocity space methods such as the curvature velocity method (CVM), the lane curvature method (LCM) and the beam curvature method (BCM) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space and thus perform better than other local obstacle avoidance techniques by taking into account the physical constraints of the environment and the dynamics of the vehicle. A new local obstacle avoidance approach is presented in this paper to remedy some limitations of the traditional velocity space method. The conversion from Cartesian space to configuration space makes it possible for the proposed method to be used in unknown or partially known environments. By adding the beam width into the objective function and combining the proposed prediction model of collision with the improved BCM, not only does the method inherit the smoothness of CVM, the safety of LCM and the speediness of BCM, but also it can realize smooth, safe and speedy navigation in dynamic environments. The comparative navigation experiments executed by actual mobile robots in both static and dynamic scenes demonstrate that the proposed method is not only feasible but also valid.更多还原