论文标题
在有多辆移动车辆的情况下，在不确定和动态环境中的在线计划
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
论文作者
论文摘要
我们研究了在不确定的环境障碍下其他移动车辆在场的情况下，移动机器人的自主导航。我们首先预测其他车辆的未来状态分布，以解释其不断变化的干扰影响的不确定行为。然后，我们构建了一个动态视觉的到达空间，其中包含机器人搜索最佳策略的概率高概率的状态。一般而言，由于车辆和环境干扰的动力学都是非线性的，因此我们利用非线性高斯滤波器（无知的变换）来近似未来的状态分布。最后，将远程到达空间计算和向后策略搜索迭代直至收敛。广泛的仿真评估显示了这种提出的方法在计算时间，决策准确性和计划可靠性方面具有显着优势。
We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturbances. We then construct a dynamic-obstacle-aware reachable space that contains states with high probabilities to be reached by the robot, within which the optimal policy is searched. Since, in general, the dynamics of both the vehicle and the environmental disturbances are nonlinear, we utilize a nonlinear Gaussian filter -- the unscented transform -- to approximate the future state distributions. Finally, the forward reachable space computation and backward policy search are iterated until convergence. Extensive simulation evaluations have revealed significant advantages of this proposed method in terms of computation time, decision accuracy, and planning reliability.
