移动机器人动态避障算法

把滚动规划和径向基函数神经网络（RBFNN）预测相结合，提出一种动态不确定环境下移动机器人局部路径规划过程中，针对动态障碍物的新的混合避障算法.利用摄像镜头采集动态障碍物的移动轨迹，提取形心序列，利用RBFNN建立预测模型.在移动机器人实时规划时，根据当前位置在超声波传感器的扫描范围内建立滚动窗口.当检测到动态障碍物进入滚动窗口以后，才开始进行预测计算.根据动态障碍物相邻时刻的三个时间序列值，来预测障碍物下一时刻的运动轨迹，从而把动态障碍物的避障问题转化为瞬时静态障碍物的避障问题，实现实时规划.这种算法能够提高动态避障的安全性和规划的实时性.仿真结果证明了算法是可行、高效的.

Dynamic obstacle avoidance Algorithm for the mobile robot

A new hybrid algorithm of dynamic obstacle avoidance was introduced for local path planning under an uncertainty environment. It is a combination of the rolling planning and RBF neural network (RBFNN) forecast. The moving trajectory of the dynamic obstacle was illustrated by using a camera lens, and a heart-shaped sequence was acquired from the samples. The RBFNN prediction model was built based on these data. A dynamic rolling window within the scope of the scanning ultrasonic sensor was established according to current location when the mobile robot was in real-time planning. Forecast computation was started when a rolling window into the dynamic obstacle was detected. The next moving location of the obstacle was predicted based on the three adjacent values of time sequence. Thus the dynamic obstacle avoidance issue converts into instantaneous static once and the real-time planning was reallized. This method can improve dynamic obstacle avoidance and the safety of real-time planning. Simulation results show that the method is feasible and efficient.